{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-11-20T02:02:13.071114400Z",
     "start_time": "2023-11-20T02:01:37.981924100Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import datasets,transforms\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "data_path = 'F:/机器学习数据集/代码资源_PyTorch深度学习入门/CH4/data'\n",
    "train_path = os.path.join(data_path,'train')\n",
    "test_path = os.path.join(data_path,'test')\n",
    "valid_path = os.path.join(data_path,'valid')\n",
    "data_transforms = {\n",
    "    'train':transforms.Compose(\n",
    "        [transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),]\n",
    "    ),\n",
    "    'test':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),\n",
    "    ]),\n",
    "    'valid':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),\n",
    "    ])\n",
    "}"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T02:39:26.388313200Z",
     "start_time": "2023-11-20T02:39:26.204659700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "(80, 42, 42)"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset = datasets.ImageFolder(train_path,transform=data_transforms['train'])\n",
    "testset = datasets.ImageFolder(test_path,transform=data_transforms['test'])\n",
    "validset = datasets.ImageFolder(valid_path,transform=data_transforms['valid'])\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(trainset,shuffle=True,batch_size=5,num_workers=4)\n",
    "test_loader = torch.utils.data.DataLoader(testset,batch_size=5,num_workers=4)\n",
    "valid_loader = torch.utils.data.DataLoader(validset,batch_size=5,num_workers=4)\n",
    "len(train_loader),len(test_loader),len(valid_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T02:39:29.010682500Z",
     "start_time": "2023-11-20T02:39:28.939684900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "def imshow(inputs):\n",
    "    inputs = inputs/2+0.5\n",
    "    inputs = inputs.numpy().transpose(1,2,0)\n",
    "    plt.imshow(inputs)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T02:39:30.360994400Z",
     "start_time": "2023-11-20T02:39:30.345999400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "羊驼\n",
      "羊驼\n",
      "羊驼\n",
      "羊驼\n",
      "熊猫\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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YjJvhV2sqW4xzQyAGweZsA7OeKDuUhqLUtF3COc16Ewmhx1pPYWqqoqawGlsE/HpDpKOq5kzrBW2/ZtOuUEITUw9EtM5zOkkS+sy6JQRRDJgiYuQUqxQxKLQ0HD1aIoCqNPjUIoVHK4NWU3ZKiYiR1kUUkdpahig4fbgkuA0xTvhu43sCUKy1fPrTn+Yzn/kMf/kv/+XL+z/zmc/wS7/0S9/1cWqZmBryQEaATO9LCTEmtBSkEDPlL/NCXJhEpQRFAqPGBSPlvIhICSWAmLBGgMmgJaU0sjMJmRJpTONokR+fRI81DXNzhu4/oE4bJlViaNfEEJApMjDFqDnJD6yXLctly61bJcm1GFkxmwiC92htUbagqCx97BAJtJZoLUjREPo1ugiYMiLCCao/pzTXaP11NuoGnXmKgZySUgpUSmgShoTFo5NDCEUhJLWQeCGRAhSJqMGJPHZ9zCkNIwImBhKCgM7MFDl7FqLAh7yI3L37Dp3rMLJCiQP+3M//HFIbvvCVAFJxmhw+OK7tHrJabTg5OkEqw7o5QUgDBGbDaqTSLe8/OKIQArxHCs3J8RlFqZnNKsQQWC1XuCCY7+yRhMyLq0jENKawhLhkBy5ZjpFVgO8MPp4EMR9lSrb3bxmNbwMyAgiJIPPYCJlzjYn8niTiQ8d6kiH46Os9CVpsFBhpOAs9X3rj63TLDnzEjN9rNZxTlTMevnPC+dERSiu01qQEWo9sRBQj4JB5kVIaKQNCJKRMCClQckyPkRBCkWKgsIZoDCBRaJKINOsNRWFZ7Cw4PT4h+MTQD8SYUCMrsrOzx/vv3R/TUHkxLYqC1WqNtRbvPT446rpGhIhzgQR4mQgxUtc1IYQx/aJymqksUVIilWLoe4ahG9kPAwKKwtJ1LULIDILIICz4iBSCPoS8WKWYQZPIi4M2Oi+sIRJj4uHJQ4RJCCOY1BNW3Sm3b91i6AwxTljszbl+/SnuvXOfb67exEhDGgJx8IQhcnT/HrZINOsLXn3u46AiuhRIq1FSkUj0vkcajSkVCYWxBu8cIiliDHmRHTdWWiqkyMyQlIa6nlDWms16oJgYSinwzqENTHcMQxfRVlDWGqUE50cdSqucytYJW0gSgb6LkCR2ohDEfO76nmQHRJQINLNFTpX0ZUIohUt5jKQUBAc+DQjtSMkxdAPB90gxsowIYvC4vqHeMaT5hK7p8UOiqmvKqaWuC/b3Z7z/zkMW+5Y7zx2wWfacnzbE6Og2Hlvlay7EBEh8iDgf8D4ixs1IHqtE8IGIJIlIlHlTefrAMfQ9z80CXVxhtMTHiFQKKRPBG1IsEDKilaQuLVJJYvTElEZ2UFIUc9ABH3q0EYSUkErjQ0dRwlxYrJ4gpcQ5R10XVLbGxSXIjtrU9E2k0JoQOpqNJ0nPYlYz+A0XXY81Fj0YdmSNxbDpWibTCZ27wCTD6fKCopfcvHXIJnSomEhtRyUss8kUlxLd0DH0DiV6nO8xxkNytM1ADJFNN3B0fMHgAlVZU5SG2oKuAoUC7xsKDc4W+BBwQVFbTQyBFHtcb4lCk7yhrixnJ6cs1yfcvnmDota0fUtdTjGyRAVJUSRSaCnUlIPr1+mHDR+eVf/k+J6leH7lV36Fv/7X/zo/+qM/yp/+03+aX/u1X+O9997jb/7Nv/ldH8MUkkIzag4Ew1Y/4YGQT9wYsjBDBoGWCWMz+JgkUC6SksjpjwRIgRZkYBMTcfzJmouYLz7y7oaUMGmglC271SmFfEC/eUBhHKW0uLVnfXbOYj5BGY0xJS4OdF2PazqmNpB8QJa7qChx3QlaJLQtEJVERI9IASUDUkuiAmklybUgFbIsCc6Da9D+LabqIUKsUCmgikMUCo1EEJEpIhMI4REyIJNHCYkBUHmBlCIzT6SsqdlEzRByikQmPy6yHk9e8CICwUgZS4UTliB6oOHR6T2MlfzYj/wpPvbcC7zz/j3qWuJcz9kxtO2G2c6cVdsiDZhC89yzL+PDhjfffYemXbGzM4focb3j4uKck4sjXv+h1zAIdFJ42VHv7WDrvCsXMWbw9Mewhltw8tEUyzaeBAjbvz8KQrbH+GiqR4xaH2A8bx7rJ7balfTEcR7/3+P7Pvo6Umb9SVKKTsC9d77Fbtvw9K1drt3cZ3dnhjWK0G5wrea/+uwf8I1373LzzjP4cUJXwuAHTxrFSUJCEAKpC0g9Kbq8Ww/gcTnXbi0heAYcVVWBlNSzHYwQnB4/ohs6hFEsppGiqLholvStz3lEK5FFwdDnBUSrxGQyIaaAUlkLo6Sh8x6hNNoUDMNASFuglhc/rTKQ0krRdmM6JgZ8iuAdg3OZaRkBlxCJdbOEMe1jjMUFn/cdKeUFGIHShpAE2hi0zo+Z7s64WF1QVAWLgwUhDfjQ0fcD06rGlCUiCA52dtFI9p/aw1QGiaNbb5gWJfPZhMVsijWeSKQ9OqGuLKTEatWiDCgr0dERhkDvB5SJRJdIIdJbh3cDWpcZVJE3TkPfZjYg+VHDBFJognNUE9A6g5HgA5u1IwSF6xLRw9B6EPnaIkHfe9o2sx3aSvomUk0sUmY9TMrEKUpLdg4LCmMpy4Kqqkk3BWEAIUv6znL6cIORKn/n6pSQLohJYu2EmAy9i/RDoBsimJqdwxmH00QM8PDeKXeevU5dV0ihSIA2huVqyWJR8NTtXYbe0zQdJw8brNJZLycVQmYmcBgGQgSJJ8a8QQ2JDFCEJAmB1CCEJPQCKYqR9XQ439OFE4QQtL3GmPx/SmmiBKsNwzAw+A4fA0kI+uCQYQMElPFEn8EeImIMkBJ1WWFVRT+0FEWJ95J2WNP1DcZIohiw0wKDxAdDVe3jw0CKA13viEEgC0XrWpI+xttA7ATKaoYQsVbw2vMv0XaSxdxi4wXr9jSDGqFRakArhXMRJQV915AErNaOfhioCoULDt0Hlheapu04PEhc2zvAakUhJojYMvQNSmkm5Q4pRdpuzeZkQ6DFFo5pKRnYICMM3Tnt5hSdEq7JOhiZJO1mjZOSqtzBO1hvzrDGU1ZzQiq/beP3J8X3DKD81b/6Vzk5OeHv//2/z/3793n99df5Z//sn/HMM89818eQGtBZvGkFeB+zkDVKXMoUm/eCMf2PMJmNkElQGpiXkhQSwSfcIIhCXLIJOZ/D49/kXXBIgZQSCkchWwoeYMM9Qn/B1FS4TaIZelarhsIaSAERBccPTxF6ws7ODXb2LegBYfLx0/qCWgVCe0GigZQQSSIYEMqM6RyFtlmc64Mn+Q1ESddFpLtA25aqskjnUSQGPScFldM8Mo1CWiAlosi7ZikEUozJm5Ea3SpVDG78vBDH+1KSWbWLHOk/CET6YcBUM4QoMKZDSUc3rJEqA7lpPeP44gNA8WDV0bg1txc19bRmp9rFFBU3n3qWt97+Kk0rEHrG8UVDEQfskNi0HbOdCdNpQd84mo0jGs+dZ66xSHuoKEkuEFPMKaoxPgo6nmRPngQKH027PBlPgpCPpgg+9BpCkGLOEaYEWulxNPm24330fX30/2OMH0rH6KGnOD3lL//Sn+HWc3dQRsMIlkXvuHjvDZ750sDd08es0TAMKBGRZNFpHLUhxhYoXSCCom3WgMzpmpRIRIytMEA/DNiiJsTA/+iv/BI/+9M/zT/6T/9TfvOzv0XSgouLU7Q0WWciUs5/28zS+JGt0EZxcLjPcnmWAZyIdH2LFBnEKSkorMYN3ch4RYzSkGJmJmNg1LGjZB7/pm0vU2xK5NTDMAzEELDGshUsF8ay1UenGAgxogtLPZ+SpGS9abj13NM0bYONFfPdBZ1rKCvNU7dv8+jRQ5p2yfXZgnffusuzd57h+ede5sHpI5RVfOJTr9NvGrrlhugDzz73LFoXtF1L165J5HTD5rRHlTmXr6RgMinRUtL3A37I33NIPp+7MWQdjQCUwg+jUFcKQgxIpVFCIoVEKInSiaH1bC4cqlB59z44ogNEYuewQiTBZumRVtJ3ERE95VSjSwUyj520iRgEWhn2d+dMdiyFrdk0DevjCyaTKaRIVep8DW4cRaEYziXtBs5OTkixoih2kTKnlLXM7HIQniEMzOspgsTk5Qne9Tx6cETTNbhgUEIzmQs2bUOIhhQkRVnwzMem7KqnRtZWEFPMc19KRB9BRrwPbDYNzkfKssILhRSRaCIhdhBd1qXFyGrTUNhISoGUYFvhOngPTuG8BHxeA0aButKCrgs0TQMioY3Kn9FoRMq6J+89CUfwKrPPPrLerJhNSuqyZggdy6ahLhw+6Jy6KyyuE7hoiakgxg0xCaxO9HHNZC7Z3Z8xqQq8U0ipSKlhfz7Hhx7XelyEcqYxOhJ9JMRIUQhKU+ODR5nIcmlwQ5GvCZ2obEX91JzBTZlX15iaGS4OJDlj0x2xdmvCEJhX+wydo2lXCBWJyTOkyLI7x5gJWhlSHNibLwCNSBKcAqeJPuKiYmp3EbKlqDTO9wh5nudS5ny38T0Vyf7yL/8yv/zLv/z/9fOjiEBES0mtBNII2iHlNIXKaZ8uJfoAMQlkgNhFjJBomUW1zgtiSDCmawQCsRUpSvGYmk+MCueQdzOpI7ljXPsOS7FC2IqNayl11nNA5N4Hj3j62WfwSjPfuUY130NqBUqQ1KjslQppLanRyEISuobEkJX+yQMJhScODgpFFAmlDOvTc1JIJO9GRaxCDGtUCiAbQvUCQe+ihCSqzAwJkT9NkiGLZJNEhG3ZUyJuq35SIhKIlzO8giiJ6XFVkEAQpSCOAoPSKFxMtO3AdCZ4dPEOv/+V3+Xa/vNUlUALTdtHXJKYuuBiuaSsK6IWKGMQeMrScP3GTVCS+4/WzBc1/cMG4QZUaejageZioHWRGy/usViUmKYkbHL+eVtR/8cxFB9N9zz5exvfKb3zJKPyUaBz+feYI08pEgEpLVKa/H9SIOKHAdEfl07asieQwZJMEjrPzWtz9vYn9O0F1VowaIFygT70qMmE157b58t3lx8CX1t0JCX4XCbBT/7UT/Dv//u/hAjwf/7P/0986UtfROssPL194xqz+Yyjo0eUZcVsNmO1POfTn3qd+bTkP/wP/gpf+toXeXR2kkV2KhFjPke3CiApYXdnwaOHD5nNJty6dYO2vaDrB4SICBFBZtG1VtA5h1Y5BaVGEa9WkqAkQoDRWVhrtALU+DszCYmIMRprFClFCGO6NyXSCGwiCVMVFErhY0AVluV6hTAaYTQnD8+wZcH5eonRkuXFkn5Y88ILz3L06ITNck3oQERDVdTElAXag+uY70/p2w1N04KBvu3x3hG3i1aK9KEjrWG+X2EqBSYR+oi2GghoLTAmf9Y47qSksoTYY6wFNx5v6IBccTRsPMn1TOYlF2vH8mhg79k5WLBzhW8TRkdsKTFGEyP0/bgp0eCGSKklUiaGPtGve+p5xY1ndkBGEtB0kYSma3tE8vk99IL5DOaLipR69EwS5YRCH+DDGueW9P0aOZtgjcFbh2sjbnCcnfQ456iqgtXFivV5rhjyosH1nsIscF5g5je5OPuAw2tPE8IDvDREUeEJrNsNZxcNISn6kNBC0ftA6wUxKXCJKEJOY3pQ1nD7mX3azo2pTYXQmhR6gk8QEyFEYjL44Mf0lCCmAYLI859P7E52afo1QgWEMHgXsbpACk3j2/w9d4EY1yQkTefpe8/OzFKVE9QgCW0kJomSaqzWG5AKVFR45dnbmSGUx8Uma8ikIRLwIYuIrckVMutmRalnRBRGmqzf8x4pFNaWKG1puqyL0UKyM58yr6Y4t8EUHkRgVhm8q5BEhHSkOBCSZ9VvOLo4x1pDWbaYqaIsBIMbSG1gGBzJWAgNShoKVWCsIXlIXhC7HhkTk7qmnuzQ9h2DO6eoamwhiHGNtgEhfwAYlH8bIRDIJNBjaVVIGaD0Q0CPC2cgEsmTmkgC4SUqCpyLhCGXDAsEIoqRF9hKGkeeII4iPwkpxMyIpETwA5uLB5jocCTMsMZ1HWsGLk4eUSqNMBVO1Exme9TTCUJHIACSUZqaF3tTk+pAUh0ieEJzgjQ10neZKSEhyhkpZcV3PyT+xW9/k7fevc+nP/kCn3zpFqqoOL9ocAicHhj6R8gqocodfBJZwS8jXqRMe8dcdRHIVTuQOSLYLsjiQ3qTKGRWnAsBQhFReRGQWWPRLJcsdhXd0HF6vqYuC25df4GmVdx7+IDVytMPHlNZyqlFRMkw9MhBYo2lbU54792vU1W7vPDy8+zdr4n9hnsP36c0hsiATmLcOXqCr/j6l9/mucOAjocZVI656JjyZCy2tNF3ABzfKd3zUdDyx4lkvyPrQcZ5UXApFBRCIkXWdUgpLgHKkzqW7e0nQUsI4fJ9RCJCB374U5/g/J03adYrbl+7jTqY0yw3tKKjUonr169Rxa/RkvJELDxbcXhMAW0UVVWyu7vDtWuHPHvnGT7x+sf5tV/7NZYXS1577TWuXz/k9q2bPHjwgP39fVarNXfvvkt0DadH93nq5jVu3brJyep8TGNZitIikkAmRT0tMKWmrArmixnGaB48uI+QkcmkoCztmIbL15pSkqrUOS2WEkYZYoj0Q09dFlnHA7l0eBwvrbeVPhofHIz6njRW/8SUtScxZHo+qHwKRBLSGtbt5jLl1bUd1liM1qybNQOKwiqOH52xu3NB07SEZkBTYVXWiUymmjff/SarkyWvfew16kXNydER89mMFD1Hj07Y399nNp2hlGY+n+Ojx5oCow2u86SocQN51+tSZnmjJ8ZEiIlYPYVbvotVEt9saJdLCq1Y7Dm0tSz2JzTOsTxzJBTV3DL0jvXSo1TJfHZAEicMPuFDIAmJEpZCQUgDSmf2SgB2ZALm8xJjE4OLxCixao/N6pxCViymtzk++iZBRtpVQGsoCsMQUi75X9ScnpyxWbc0mwa3cCANkIgpcn52jtg4nAsIFCokpJdEKZFYdJKoOGOzHmii5uxh4NrO0/ggCWIKRuKDoyxr9g4OOT4+xQhHZRVVDSEt6Z3P5esIiqJCG0tVe6b7+zTnPUYrrDGkmLVwTdcipGG/nCODo9KSmGAY2gzYkRhpGIYWpUsqGVi1F5S2wChN8AHPkKtpjGK5cvgYQGTt1HwyRaWKvo/0zjEMAicDUgwYnSuEIG+AczVNZgELLVj3Pb0MVOUUvENLQbuCwlqsKZjVc1rtudisGDpBLxzTWiOFxJqa4DM6FyTKYkrSFcZASC1JeEIAITUJTx+X+BC4WHV41yIRlLJEJU3ftTR9izEKKTWr8wvqiQQh8DFSTCyNb7FYoosoKUcw29I7QVUWGFHQOUdZjnPkpLpcb76b+IEGKKtlREhoVcoVDSnhoyAEgUwZUATPyEYIogKhBL0DIyUi5rJNhEClLXvCZTpEppFViWkUO0KSGkRg3Q40jaA969D+nP2ZQqeW0wd3uX3jOvOdfRb7B8wWC6pKI3CwaTLjERPSGFJVgSlI1iJ1SUxzMBu6kweUO/vIMCC1xHsPzuBWHbqq+eI33+N3f/9t7j5Y8pWvfsBf+ys/wc/+1OsEMWWtbyMnN7CqICYI3iOSJIicksnpnm31Rhx/jyzKk+vupWhCZqocgCymDEoRRS5nDjGLMXf2IhfLc5xrKWyNEjVWTqh2F/BiyVvvvJNPTOGpqwUHe7s0TcvFuuHhg2MuHj2krAV9v+EbX/08128+xf7BM7z7h28iU2JvMmOiLfe7C2bXF2hjkTGiEITgkZGcphJkARtZLKnGip4n9SMfBSAfZVy2wOXflCv9UKomD08GbeIxGNmO6xaIbH/+JLCzfQ+XZcJKUs8WvP+1f83p3XeRTx1x+PJzvP2vv8gmCW4dzti7/Qo/9drL/OZpnii01gQfx/cYMVpR1RVHR4/4nc/9NpvXl9y6cZP/zf/6f8WjRw+pqgqZIpOq4OMvvUgIgaIsOHr0PF/84u8R+pY7t2/zQ594nbfvvk+KDis1RlliATIpkJGYHCcnR+zv7+EGz2azRiiI0aOUyjqSUUC53QhcjncE7zwpeAKjbghBP/RorUGI8f6c5gjOZwZfwTD0lKYEsjA+i4UNvQgoo9HWoK1l02yIvQPnaDcbKlugjEJUE2I0DK5hUu+wvGgAxer8gv15QQoB5zs27RlP3d7nrg9suhXPPfU8i8mcia3RXSR6Tz2ZcOP6TYw2hFYQY2KQHgl0veP8UYOQEm3BlHk3TUz5NQZP5wJ922EnFUIobFmO6ZseESEESRImA5rBE73CnUbKWUFRTajLBav1Of0QAU/wAuECRpbE6KkmmnoiKUsDImEKRTmp8T5ycdZycdJRF7NcaWQMs8lNVsu36DeOpDyTmUEagZIDxgaiXCGVpSxnWDshJEkYAjFJhFLMJ1N0EYlR03cBJTyhH5iaGonETDQxCoypKArL/mHBtGp5776kmpfYvR7n16gQqKRD+IZ+01LJCi3BtxdEH0m2wBqLTANWFWiZS9wXtye4U413gnbYoEQiJYlMCS17lIyAJSaVU4EmYVQ9CsgTG3eKEgqtK6y1DMlD1JAEWjp8UNRlnqdDCGhrKYsCHxr6LtEPCd8nQhqYTUu0KhCUmZWPiVlZIFIk4nh6P/LBseT+RUdSEh8lUQ6EDvpVD2WNki2SwKRUGBdJwlAoQcIT4mYs+ZV47wghM5eJBikNSZY4n5AijexLorIlbeNARHbmBcF5VpsVITrOjjfs7E0p7R5KDUgh8CGilWHVLjP4FhOMNvg44AjYwuN8RMgeaxQyRPpGkFRNbQ+Q4jvrAL9T/EADlOZCkoREyywMAoHisSFbjJKQRg1KguAjUUZiFHROYmRmYBR5IHKKZwyRy2vzhi8DlEQiJEGIkKRh5TVn64G9uuDRuuHrX/s6T1/b41a5T1kvmBkowwWxyUJVsW4JzZr2YklV18j9feRij1ROiFWJ0BOwBUVZI4REzWY5X3q2xG1WuMGxPF/zL3/993nv0ZJelAxR8pWvvM1P/unXicUcwRQnZiQMUie8sACYkM3bslg2fybGDI4Qafxz64vBuLCOmgaZmZ+tj0waGYqgwI2L9NAN9F1HColV07OYLajKCeVkxsOzY9CRqizpB8fRwyUXJxfs7C4gJRbzGacP7iJNyWrZoa1keXbKzfl1jFT4CIVS2bhKJM4uVpRTRfIdQycoc/4NLqt5tmm5rQ1bVtbEmOAjgODJFEs2LXv83G18tNLmo3+ThyrLpxOPWRMyWAohoIQcze0+rEH5KBjaGpE9Bk0CbQqq2Q714pCT1Rrz7Avog+v48k0evvMBe/u76MMb7Fx7D3l2BimQ8CiVhyVEicHSbBx333vA88++hOtbbKFwbsPuoiJ4R99saLygKAq6ruPh/Ybl+TlH796lqKe0z3bcvv0Ms3rBxekJQWQ2MTqPVIrkU051GUhRooVEaMPQDdlbxVh635NiQktLiJGYEkroUZAYmU4qXD+MJla5dNhM6svS4xgjRWHz6Aqdy48Z01p2LCWWmWmbLKbsLQoG4Rhi1ihNJgWzSUm72SBlwBYVzgeGwRN9R1GaDGh0yRAGtIRCCApToIVGJxBh4NbhnEqU+E2L63tSWSOLGllU+JSI5IqX0IHwCrSkD4HgIyJGmmXHZE+TWk3osrg6+AFSyPqHNOCdRCnFZHcXIyN91zC4jq7pOV+vca3PACdsz6dIt05YFiyPVkgbMaWkqg3CBuLQMt2VLBYlRSFwfiARcT5xfveM5dLhHMSQ0GnDZCqppwatQdAymWpqayiMJbqeGCRD3/LwwRLvc7m2j55+6OiHmBfrwqLVlOAiOlqqIs9LTndIwKiK7GljqW2FkTto+zztquTGznNMbJmF3DGnzLp2AyliVK5e6kKg63tiBG0UMSqiDzjvmaiSW/sfQwv44OwR3aYj4RFGMa9naJ3GAomIVNlEsS5GUwXVofU2XashKXSKJBymVMSgWW96BB4lBUqPGj0h0CYSU0vXe7SumFVTwrBhPtmlqmp8bGi6jsENFKZkCGuUtNTWcNYk5tWc3hcoUZBSyTC0lIVGaEHSgRAGvOsZ4sAQHErlis0kAjENSDoKbegHh1Q9h3sLrLIMTjP4QKE1RheQItoITs83FFbiosIYwzo6koC2D8ACoyYsm2MWC4NS0LqID5Fms6asMlgOMqB13lT1KTGfatwwoIYChkQ7NBSmxKcmg7PvMn6gAYpOAjmMZZ0qm4gBWbCYYOseSyQzIwiGJHB9pBsSlVIYDVpl4zEjyLXgIiJiGsWhY/09iZSyiC04T4oSa6bsH9yiXx3z9W+8zdmjgTs3FnztG/e4mH6Av2bZ2ymZ7szyQtk4Vvfv88Xf/T2mRcHh87c5fP2HKG8/TZQJURiULDDTPaLvUaZk6BoCYGc1IkL/sGHS9XRHx3ywcly7fkChLacXHav+jFCViCiR9TWEqnPSKkmCyGBLIsd0SAYdKqtR8kIrnyjPjdt0R4SYxmqgQJCJGDWERIoGYt4hJqF4+unbpEHy4NGS67fv8PzHXqZtHPHddxl8pNyCxejpvUQkw7X9BSkOlHducHRxxKZbI9aKi+MlxlliCJiyQsrEw/MV88MZ1cKy2UR0kCAMwQdEhKQfJ+i2rFeS8kOY5KOlx9+mN9lSRyKfM38c0/FkSiaNQMiJhHYJ6eOHji1GAW2CDzE5Hz3Ok3H53JQwOpBEx8d+7Md49pN/Cj3bxxN55ecsL7kV5fSAuGrZLyzWaGKIH9JRhTimSXzgYy++yE/82I/xyksvorXI55dzGC2xWpCiJ7iEEpG+3XB2coQSkvOzc4qi5Gf/nZ/n/bsP+N3f+U1830PwhOApCzM67EqEzK6k9WTKdDbj9OiIyWSCEIL1eg1AdJFN01BYi1SKpmuZziZU1rIRDUoZUkh0rsMWGWR77zEmC3MHH9BGg8jVG1LlEt5Ms4MtLEorqrJECcnq7JyYQAYotUGEyKZZMnQDSJWrUmz29CmLKXVVENvshhtiROlcDXN+ckFZgwwKVWradcODBw8RMjEpF5iJhhA5uzjjxvyQg4P9LEYvNG3TE6VncrhgU3YolVhvHDJUhJBde4VSqJS/h77bIISh2pkTQ4NSChUsFQsoDKICbRXRgQsePwzUU8tiNoFhH8yANpq60kQvqW/IbI4XFMlFimgR1oFOiCIyPdBEr2Aw7C1uMZlLmk1Prebc3n8xX1dpkp2NRUuUGwq7x3Qq8XEFQSHo6DOGoCwq5lXF3t4P5YqwJClsNvZaNysAjCqQArqmYdP0oKeURUlMhuneNSQRldajRiexaTpiCCiRmTXnAzEEtsxZIGBMHj+ZDAfVbZSEB/KMypag/Vi+LoGAC4YQHCYlphWQoI+JkDoG5/Ahb2RhoO97nPcYaxiGvM6EkIsxSGJ0bvVMZYGPkXboqWR29r15o8KYmhg1xxctMXSIFGl7DyJRGsXZquOUhFEWJRNSDTBULI8HxEFkUusMAF2g7XNKyagsSg1hYDItAcPZsuNgrjjY28UFaLqG+f6EnR2FVTXd0OJij1GatvUUNlEVBS4pbh5OeHi04eRiw6QyVEox3RFYV9FcbJDKsDPP6bBmMFijIQUQniFGrC5RsiCKnFrsfWTwESs0qpdI+W9mpp+MH2iAYkWgEJEk5GWFipZgVd4AuwB+3IVmecJYAhohIGi9p3PZ8r5WkkoLdMrljpDZhkDKTqbj7jjFnBKw1rC3ex0/mRF2brLuwHnHex+c8I0vfInnDjQ7P/USB3oPh8s2xN3Ae+9+wL/8/HvovuPl9+7xsYsLnvvxH2Pn+ZeJag7SIqspoksj/VohdwWymGBNyfRA8h/8tYrZ7Pf4/JffZ+/GAT/5qY9z7+4jvDRMFpF6v8JqCEIiRNa6hJTTOQmFSIoM6zwphRGggExkr4hLHmlkFUYBJNsxwCMQGATBZb3Eydk5fe/Y27uGsAXPP/csF8tHbDYDMQgW8wN2FxVNs2bTrJFS8uDhPfphQdc1WKvouohSmtJaTs7Oefvdd7GFIaZAO4CsxtLbkKjrCSo42ralFBIl1Lh7HnU020+wvRgu7/jwOfQhAPKRsuItG/JR75PvJMDNDJRASUFVVTw203+iBYEQl/qS7+R78p2cFVNM4BIXDx7g/Qlx06IOruMUhKMHVJPcEqHf9JjZAm1WVNbSpXip7bA6V9q8+OLz/M/+xv+Up59+mro09G2TXYZ1tuIvjGZ9saKazwkkpnXJ0HeUdc3Jas16vebjL36M/+iv/TXOju9x94N36JsGgaEsC7q2QxlLjND6FahEJJe7aq1xzuGcoyzK7D0SI9YYGEGkMQalFWm8v/fDaND1JLWZgUoeQy6/mxgjatyhCJHPk+Vyyf3j+8z2ZhRFibElzWrD0DiEE+igGNa5bN8WhqQ9xqrsiDopqAtLubePSSUp5RLXfjMghWFztmYjHNf2rxH8wDA0DLEnGUfX95ASxhT8yKs/RlFYhBR4n+cfLQVSKqTI2gNjDClsBbKG0CdiiASZUEqzWq2IsWPXRowt+IlP/STVpELJ7AktRfZo2jIBxhqC7xFj2ayUCiUtyJz6ImZO0Yz3SW1Iow8MCQpT5RRaCCAUg5ii4yGSxP1HRzTDwKSqODo6oncCJTzLi0iUNatNg5iuefX519hf7GGsYVJPRrv2MI6LYW9nj6HrcMGjlGFv94CUoAsaKxwPji+IKKxKqKRRyiBVTi+4kBd/NXquaJW9TQbvkUmivEcZS/CRtu2Ro+ZhOq1JKj+uHwaMhm5wKGlRyoAWuCHPTWenHdpIqkojY6QPjmFwWG3R0oyOuILCZtmAVnKsIsvtCpLIawTJg3AMIdL5FiMLZkax8ZpNmyjUBGkV82KKk34ssV5mc7oUkd0jmgtHNZlilSMKgY8GKQuid1y0gVJrZOooC4M0khsHC8DhYjadG1zHEA21KlkPS5xviFFQFgW21OwXC1argd2F4nzVsml6erdBW4EuBSEZkpAUlWHwkUpDVSR23R6FkZR1j3Me1wtSlEQvSCELeLUc6HzPpJrhNhIb7bfNcX9S/EADFE3AylySqJTIwCJlV9iRQMk9aEiEACGNuf8USXi8iIgk0BFcEOhkSEqMKuMMUCR5Tdv+3i48Apl3XtWcYjrn2Vc1v/7P/58spOcb33qb8/uSn3ntJjcONH7dcWMyZzhZ8v/+3S/z61+5oIieozUE8zZOK16bTqlvlWAqmB0ijc1beCXQqSIZixCWlCxPfWrK//jmIX/27UcMzrF4+hm+df+ISgj2ygl1WRA0dEngkrw0MospEUO2XHucyRg/z9iXR8Y0/t+TqYcnAArZDC+JhIwB5R0CMLbi4rzFsUQog0BhTclX3v0yxyery+/s2rV9LlbQtZ6+j7Rtx2q1yaZg2iJlxBSKoq7Y3d9jee8EKSNny4Fbr9zk7HyFdIAYSH3L1M4JhFwVlXKKgZEF+qi2RIxAlcvP9NGKnQxKHve8ycZh2/jjzNRgi3vy88xoihYZ02gI4tiL5zuZs33UG+XJY3uRaFNifbTGPXqHYjFltb7Lptnw1DN3OP/6VzjcuYEvd/naaYOLCWsszjliCqNYLvLMnWf5X/yNv8G1gz2a9YowqLxLb5vMsnjP6mzF3mJ2adJW7u/x9J3bfOnLX+Xg2iEHBwcEH7h2cMjTTz/Ng/t380I7/mShq8LFQO8cC6NHUJ+t5J1zuYTaaPq2B8GY4hmdTWPM4sMQkELhfH68kOJy7LKBW+Y1nxQ6p5Rwzl06i3Z9PzJQAuEEQ3CcHa+obUXsA8Fl+j4zgIEuBlSRNS8uDBw/PGZ3fzfLGKXCB4d3Hokl9HB2vGRqpxRyxXq5yXbyKpGCIDoQyaC14kdeex1tdBb0K5mdicZ0UxrTeV3bcO/u+1ycHPPg+AJz8HG0NFSVzvqF6Ak+MvieelJz++azXD/czedOyG6q2hRAQsjMiV4KhRMZlIynfUopa+2kREqF9+7ymvDO5QXfWFKKWJ2Q0jB0EmsmGJWYzY7wZw1u1SF9g0qCSkX0dIaqdllvGobuEet+yUzOmJQLXMhVW4ksrowuEGLu05Niyv15AIQkiJyqsEWBLWvisMrXA4lhyJVAbnD4wRNkNthbNx1GG6yWuJD72ATvaPqWP/rKV3FDz3wqiWmFUZ7BO5QKTCdlZlOkwYcsUvVREsdrSEjwLjsoK10wnxUQI+tmQCkzCrYj2VEgYbTEOY81GqsruqEhjN9xcHnRnk+f5trBHWq7m3+qXbQuUGM/qRCzkV/vB2JIDINHS4mxAiEGQvQsm5ZNv+L9h29xvjmhnkhSaqirKa1b4aPHh46JKQjB4f3A3QenXFycc7YciNEzrUs+iKe88PRN+laxs3Ak9Ni+Iju9DsMFRkuGwWBLQzmx9H0kpp4QEotFTQqBqkhMqpLgEoOTo9lgrryrTYVAUxaKqp4wdJonXaD/TfEDDVCsTBQq5IoAofLCkpuvjNUm5ElQZLETcWQLLne9CpFi3n3ErEgPMU8k6lJbIEc9wegGktJoeS+QwmThZ4rU9Q4//bO/yOd/+zNs+oF31y1ffesBB1VLChsm+7usTi/4yhsPiU7QEPjgtOHrX3uAJbE7mXN9EEyffQ5ZTBEqkcQolIw+52BJubKhKilvKG7XNQlY+YKDXjGpp+zWNUXt8UVDFxSnztAFS+CxcVg2nRNjdUOO7O0SL4GLvExtQVbjBCDmsmIhgUgSAR9cZl+UYjZbUCzmnJ2fZWGot8zqHTbr+3jnWK+yBfze3jVWmzVy1XF+ckb0ifO2R9mG2UQhpWG+M0WIiOsdpdCklNisVhwe7nNyfsFyecLeYpI9UFIkiVE/Isa0jhBj6XE+CaQQRPJ5oQgZcCZFVgtk9kTFnARLIl3qcJIYNSVpyx4lttfXh0GGgNy3Ea0kKWW/Bkb30I+WJm93/tvbW+fUbSqR8T3lHkqSYw+3rt/gvTffhqBQXc8fvfU+129aytjyB199yOc+WMF8dzsM2S9DZtv7/b0dCmtYr1ZIIbJLp1E0zYbCaMJYLeDdgLU1KWax53PPPcPR6Rk3nrqT/VRCYFLXvPSxV/jSF75Eu26RSIIXbBpHNS3A9UhpQRiEyCmZbAAXLj9rCOHS8TfGrFcYhgE/anaGmNkTJfN1PQzDE9VN4GNCKXUJ7LL/ymiPz3jpS4mVBa4PRJlILhIJ9F1elJXRI/gZPWBUcZkO3qx6qtJR6SLv8o3CGM2knnF6doRAoJVmdb7m/HRNTGBKi9YFTdej92cIkd/TVtskhcyNOUNg6DtI+XrumjUnj+7y4N49KHawRjGRU6pao9vsmisIrFdnSGlxriKMTUql0kihGJzLDJ7JztC5mim75QqpRqF2Lkn0IaBFZnwvv4MEKIVI235ccjRHy602BBFkwOoJOzODkpr9g+t5TpRZsDz4vAns2yVlVTGZTJlM6myHIMZGjwmU0ngv0KVEJoV3gZgCzge0TFhjkNkGO3drHRIhBnxyzBY1B/s7xOBo2o6uG5BKYkpDbSuWmw6lLUVhEAQ+uH8XQWJWH1CpiFIgCoVUJVLl1GykR5tEP/T0gyeEiNbZQTmEDFCHIQNUHwJd7ymsRyuV0+lSUxaG6AcE+Vxr+kAKitLuMC/3ODx8kYPZc+zPblKaOo+tytdH3lUrEHnMERlAsp1znpgrUl58SCnx6Y/19K6jGza0YyO+ZtjQuVMuNkes2xOkP2Wz7litHTf2NctziVJ7zOxt9uob7NfPoSYl6+ZdTi7ephkeEMIEkqE0JVJL+kFS1QKBw3nPcjlgjeDmQcDFyHrjmdUFMTqKIvfo6pynsJIkE0VlIUSS9ES1BL7Pre7/bUW2E8/+HGGsepDkfjNaZG2EJPfXgZg7A4etIOUJAWVKo3X7uIOOMVPf4+tsrckQWzOw8bgJVIBEJAS4/vzHWbz5BsjfpvORr73zgE88s6BuW84Gx3nj2ZsW/MVPT7h2MENHg/QbSikJj5aE4zPktesIOwO9gDTkd2iyuRCJXKWcPMJamGm0MKTzNXWpmdcKoxwyeQqZKCuH7CuOLmb00Vx+XvFYaXGJZvMYPB4XtU30iC13MpajPMGuRCGyIZDSPP/cc7je84U33sTIisO9m3mXIXPuWciUdUJSstnkDpqmsGgrqKcFdx+smchIiipXKchcqmhLzeaipy4NH7z1gOOjNbP9HaKLebc0OHRV5s/2pCZk+75T/v7zOxaXjRTFSK/Z2IFQeGHHtl1blixlJlxkH9KY0igoFll/8xHtCuT2Ailmb4uUfH7eaDK1dendxhaYfKfGeLnp3giiE0Qk62TYyAWv/KlPcO9rn8cvj3jthz/OdL5gdbTi8998ky/fb3np49O88I0gBSEQMXLvg/c4OrrHnWduERF0645uM4CIrGIgtg3D5oK9vQX90OWUS1EwxMirn/wk0/mCoXc07Ya5tfzoj/0kb77xDp/7b3+TFDNzEch9oKQ2CCGz8HO7i1capMLHxKbrcDEglHoMKBK4zlEWBVLkdECCnBpxgW3JeAhZO5U/WmZovM9gR6LxYRjPzwAya1Gc9yhpswZms8kVGUYhRE4ruTBgbBbeKimxSEQUXJyu6VXH4e4+k6JiWkwJfWBqJ5gphCGyvFjRb3pcUTOpKk6PzkhCsNErEGL0psjpkuA9Shq0MZRjZY4QggebNavlhslsh8nuTUJhcBcdtZiwO59xeuoJ+MwKrJcIcYhgNOtLIoMrO1aFpAwYRIpolRe7FLdVezkDWhRV3sQ9wZb6mCseBQKpc5WU1oaYIpUwuf2HFhzu3yKm7JQcQ8AYO+pzUq5eE0+c52NqxTnH0G/Q2ubrUcoRNKWxF1P+bELqS4F5dI7TVUs1KxApMZ0abqWsH3HO4WOfq1JENpvTqkRGRfC5SsxHz7preOm5fWJMnJ3cx4oCQaILLS4MdN2QUzEGjPLEqCAqKmsZXCAR2LQt1ZiSDNEzuJxe9CFvcnSyCAzJFQxDhxcDzgdKvcvt/U9xc/clru/cYVrvoMZ0e7a5VVmaMDbUFCqDwcv5YJuujIEk5Zh+Hlt1ZMoLmyaU3jEZZrSbDUPfsmslQr8G1yU+Rbq4YbU5Q0hBXU0o7Q5VMWVSVmilc8Wc94T4aVb9GW/e/+d84L6FO1ujlcJQMaSB82XH3rxAiexTVBWWEICkWK0cRkpcdKjYo4Rg8AVaB0o7oR08IghUOaXgCYb6u4gfaICy8gLvxJi+yN+hEQJNpNB5UVAjGFFaoKIgjPTn5RI9lqHmKg8uc91xVHenNIIRBIypEQnjBfl4UXc+4KJAW8srz97Eco3zfuBfv3HETz874+Gm5f6F48/+mR+iCOdMJ7tMD65TLeZMqimTg0OKxR6Xbq26gtDlXkASIiHvitMAaYOMAQpL7BN93zGpLCq1eBcR0aAVCOGY6zmtlaybGp+yj4lIj9MWH/aPfRxhZIvyEG2hTU4XjANHJE8WQuSFRFeGazuH3H/nHCunCLId+mw24dHZAw4O99Fa4n3P2XJNUWp2rtUMw8DuXsV6vaFpPEKuuHl7TlAJPS9oTs6p7JRCVqyPN5BgvthhPptS9GWehEcdghj7FqRE7p/0Id1IQoZEUAqZoIw916vA+dpxrixR5NLzx8DjwykEGFmT7SCNXmhbRmVbQqy1GcFRYjufpCeOszViezKetOJP6Qkhbcq9cXpteeAiu2rB8z/25wg/7CjshOM3vkhIiXI2Jd1r82vIy2TcqIuRtF3H/UdHHJ2viBT4AWK7JvlVLmONkQd373H3/n2eeeYZQgxM5jO0tSACwbfs7O5RFlVOH6TI8ckJzSZ3Vu1jT4ge53r6YcANHX3bkJRh6POk3Q+OwXm0yOXCLngG53JlWchumE54hFCEsWojpdxjJctQxgoLKcbu0GLUm2SWCiEwpkAomY3QtqlILQnBQ8y2BCEFjMgN7ogJqcZyZyKuH9DkDq8hZh8lKWRu+phgNlvgh5bN6RItddZYFCXSJVQUxMExm82YSJPFm2K0aUdSltX499jMLgZSEpRVzWQ6QyiJNJZIoht6+k4x2V2wWjY0mxUxnmGrho+9+HxuPSElzg2Pr1nvsrJsyD48Rm8rnDIzCJn9Dd6htEbpfALHmB18y6LKgHZkDXOqNIN6KClNyKntkQ2KIzskhYCx8/b23M4gJC+quc2BGkFRyN8DmdmO3j9Oy45MrBCCIUYulmtsaVF4kAEZMyO1cUuMLOm6Nc2wwcoKYSXOJ1Igl42LiLGR6WSGRLM8P0aKGX13AS5vLl0j6WLEyEzUGAtKF+B1rsALPYaAjDlt1g2R87PAEAKzWkCZRbcqaU6bC0Lo6IbEnYMf5oee/Xe4tfs8RhkYexh5IlLbUXuwrZjMFWfjhyelOKbp1Jj2yhtxsTXjAoJrWZ894uiD9zh98IChGxBSc3DnOW4+9wLTnb1xkwA8AWiIjuAH3NCTvCO4YTw3EgrB3uSQ2Qt/FSt/i9PlrzP4DVJ4Bj/gO49RuZx6OjEc7k2oCkXTJtxQE3yid5KJNixXXT7HMNTFDZRZogz0TqNTZpe/2/iBBigqBUjhsstwIuHHL9Qx6kQEKCJmLAXLu36Bj9t8bAYgj6WhjxcG0uN00Eit5JTAyDZIweWuO0WBMiUf//gr/OLHaxa64fTklN/9vS/we2+dsTMTTIoZZT3h1uENJtMFcn+f2e4BZTVFaAHakEoLUiDT2EtEjRS20ONtS1IdoW8QKhBCAQGiyg0Mo+sgWmIXQBYICweT7JnwsDEM0VwySEBGdn9iSvDxYp1vyQ9BGu+yxfLFcomtJywmE/zCM5sVaBOZTKZMpzVFabn7wX1u37lF33uMhn5ouFi1eK+xskAbycn5GVpJTk+73Ire1ESRL9xppShtgfOe3dkCH13uXRI8SeaSzLFv8OViH7eZPrHV2mQaqvQtz84kH7s+4f2HHV9e91lUjPgQQPm2Kp+Uz5U4zh5pe+wnwK3WeZKV45b1yepl4EMAJQMWcflaW6FnGoFJrhBShJTwybPcnJDuP6LvTukfnuI3PQcvvcRrrzzPb3/tbv7sbF87+/okwAf4r/+b32Bx/SWGUPLmu+9x59qMOzuwH9d89Y23+Z3f/X3a5ZLXP/EqLzx/B5LjqaduEL1DuTkr5yhu3EIqS981zBdT6llN26xyU84QcMMwSqckRmdDKzHuqo3OqTqtNSKBd25MNWZKu64qhJS0bTta6GdtjLV2BEUJMTKlWulcYRPS5ZhKqQjJYwpLURZ0fYMtCpwPNE07pt9Unux1toVPzjPfmePDgEaxM5lxcnRMJCGszizP2CgvaUU1m3J8vCYaxWQ6x/We5ANVbRFDQEcopaHSJpdgB4cn96sahj6XYY9pnixMjbihx1YVQz+waVp617HZ9ISYWF58wOnFmq4b6Jo18x3Jg4cPc7M7IfDBMZlMMcZCymMdyQtwGNPdIYax4WhetILP+pRtx2dGmwFGIXDO2uTrxzlHHzTKGAopCMnnqqnLdOTjBp1SqlxZJUVOnw5h1P1lG/+UwIdcPi4JlwybGAEcMRLSgBCZnTncn6OUpe2OeLR8n2bYkESgcRcUYpeFPaRPHV3v6b1HhoIkNKUtWK0uaPsWmXomk9382i5gzQ6KjlW7xBYllYps2gYXNVZmd90YIylAoQ3SBGJMaG1YVBoVS1xIIFqC9wwh0LslSQomxQ5PXfsUP/Xxv8C8mo0Vbvn5CIlQlhAiznVIZdC6yBqVaDC2yL3ZtP7QdJxSBidCaoZmyfHdd3j7S1/g/bfeQZI3xLO9A26/9CJ3Xn6Vsp7kQofonjhINhiN0Y2O17m0ddtxm1EaIYhUdsLrL/wZ2qHjzXv/jH7wSBTWZm3Y2dohEix8jzEJ7wN+6BHaMC/nxOC5OGtp2yVNo0niXWYTSxAVPQ0yachd4L6r+IEGKAfao1RgIyTDKD9hlEKWOvtLNsHn8sKkcjXOSNNLsd39psvFI2tMeKwNeIJBieQvOftpP06FbJ88vjR1ZZjXe9za2+f6QcVMwW9/7guYcs6dG7vs79Rcu3GNZtOiui47cU5sFs8pnSdFKcddxWNmIy+cIytgp9meP0E3dLiQd2KzwpK6BtdeoJ1GqN1xwjpjMwhan1kU+cQlIOITf1+CkMyYCLFNCD1+H0nES3YgIfExl/+989b77O1f59q1Xa7fPmA6mXD86AOa9QbvPbu7Mx42nrffeMRiVzKdFEQXWdQL1k2PIJvZzeqKECNuEOzs7nJ4sEtzsoJ1n2v5RV78lsslg5FMZEclq8fsgxQgxjIukY3b0pjqu1zcQmKhAzdmBd98sOK56Yy7/oyHfT3uasiGfPExawKPmZI8DumSSxLkrtH5vIgIbUBqtMzpR6keMygfjTQC37yTHfdVSmCVRcgJF62gdYIy9bz2yh1uqjPOvvE1Lr7xBURh2HvqaUpb8KM//BJ/9NYJ5yPZpXR+/eACQhq0qXjxxZf5iZ/4SU7PB7751vv8wdfvcvATr9G1x5RlyeLgBnuLfV599XWuHcxZzGaIFCBEplVF7x2ubylqyWJWMZtV6FJTpJLMInqsscQQsdZSFSWikIQhl1+mlEbLcUnwubJHKoWWkhQsRVGCeFylI+WYalC547AMuemgC57CmFHToghjqkEKSYyjmWDwWSg7pkFE4hLAFkX2OBEJtNFYYygKQ/QZ2NqiyBuQ0uZKGZHF0m2XGSrnHShJ2/dUk5q23GCsQQAygianAYUQ+OSRSLrO4X1uwJg1FtD2A1pZposFT925w/npGU0sKBaHiN1XcENPaS3Xb9xAakVdltSTGqUNhTXYInfeLYps9DUMIzMxnocpZPDTNC1aa6aLOUrIUbhpCWHADS4viqEDkUWeSqmxNUIGza0jV4PIHiFAmWxEpsa0hDGPNS4hDvRDh1Y2sy8p0DXNKDZNhOjxMbC7c4Drez7/h1/k9rPP8NTNp/DOE2LCFAVuZLvPLhqMmCD6A9YnPct1g1Q1R/2S+9LzwnOvcHb+kHvLo2xIGXO/o7t338G7wFN3XuD67RcQd0VmFqVF6wnKR4rkcH2PzXtDIFcURUFOsQhI0SJlAUJQ2IiQgb4fSFQZuCiDGloKe5sfffmv8My115HR44durO7qiEliigki9rl3j3MokzKYqqYolYG72DpNh3CZ/hIj43bx8D2+8Nnf5O5b7+B7hzQGtKaa7/H0a5/gzosvU05qUorE4J6YxxPEcOklkxersT3FmN4TwpDkdmMUqYqaH37pz3LRfJ337r9DoS0Bi9Y1wTmsiiyXCSk9/ZA/R4qggyDGkqmc431i2GTGZrXqKCqH0oZmHQjx4DvOhd8pfqABSm09qki4IVtEJ5F3mxARwVNqlUVpSAgRz7YkcQSVl2kegPjhRSQxpkJGtoQRnBDHhWoUWF3SdQkTN9SqR8UegkUkye5Uc/2wZmYrpmPlS9+37OwtELrEDx0yFJiiQBgFSl5OMinFS4Qr0uNKj6QqVF0Q+w7vl4Q4NtJKCas1wnWkbgVrjVCWQRpO15omKsAj4mN9iRx3/o/LYhmp3cTWYTZu0xpCsK36SSk3V/Qjk3BweAOP4Ohig+wKkCVCSs5XS/phQMua/QOwNnd4HLqOTdNSTQqMkfjBc3pyipSGpulJSbJZdaR4wnRnStP3FEpmK3AlODl+hBOGp6/vXua6U8pVPCnkxYiUGyPKLcAAIpoidrx0zfLWRYeMkTfOl/zQzg7/6sGSDfVlf6HsXZKeyPuyVeJwSZekfIZsgXGIASFz07oQtt/h47H9Tt2UM8GwFdJK9vau8/Krz3C8NPzXn/kWrpU8f23K88+8wHD/C7Az5/CVF/EJYl3RF4rZzpS/8b/8n/Cf/ZN/TlEUJJ97VIkkeP6l1/lz/94v8Bf/B3+Bvf1Dzk7X/M//w7/If/yf/BPeOlN86sc+xVPPnrN/+wVsiDx185BppdEiQozYekYYjf82mxVSwWI+4d/7xV9guVnyhT/8I1w35M6zKeGHAUavihQTWmeKP6RIVVVZf5EEWmZwkvUO+jLVsmWRLkWxUqBQl2kwIIsmXcQYgfd5Vx5E3qU3zWYUPif6rsOaInv5jDqMzWpNSgFrFAqJbzpMYWjWDUpkgbwpC2RpEYNHhgw6fNvz4IO7+bnKMKlqhrbn8KkbuN7hQ8jpEJlTTAnB6cWAc+cM3ZKhXzMpJ9STCcZolC6IJutzJot9bD3DU7PqBPPZjbGSRKF17owc/EBKPlffRAi9J8ZA2w2X6bC+b0kxa1KUVHmXLBWtc7jzC6TMrruFrXJzPe/xzSanFANj6kjhhi5/F9aCtNkV1liMUoSYtRnbzUsGcYxsQaR1G9quRyuDd7mD+zAMRAIKS9f33CsuePjgHv/FP/kvefrZ5/kzf+ZnuFidY4op0+mMGHrWm4Zo91mf3ef8+B7f+Oq3MEZz59lDIgEpFeePTvDOMfjs5ZNSoihqmqbh8NotPvbSq6PvU6CWF7RhycVG0gw92kRsLZBBMrie1AtQOouww9a3qMQFD1HggsCFMU0WIwiFkQV3rn+S15//i9zcfxE/tAxDS99uWJ0fE5Kkmu0RupYQ83ub7hygTZmrpbbgISVkHNeU6Ikhok2BEIF7b3+d3/+N3+S9dz7AKk1ZFigk84Nr7N++w/5TT6GMxA0tpIjapndSGNPdW2FtIIUBYgAxamHy5J+vLeToaByZT67xqRf/I87Of42mO0cIy2YTqHRFpSMiBoYmIKJmPi2JPmJERdsG1hcbhs6xmFUUyeJdT5s81mq8+3Bl5b8pfqABio9QEJno3EHTR8kQExoxUswSEQMuBhyjCDbGzLTE7YI27jZSQIg47oLFZXpHkit/AoCISBIyqyzz5ZkAETLiac8ow5K6BiMCqW/ojo4IQ8vgA/Zwj0qUaGlxCeoqlwam4LLlPhGt5ehTMDIoUiBEkU+u0YRKCEk0BuFzB1etHSElXBooJxpVTBGhJmlJwrNylsbniSXP24+X2HA5muKSSdqCorywqwxQRkSTRpvihBp3/QnnAhd9y2Q65/yspUgCKaGc1JhpRdN39J3DWk2IHSdHHcVkwu61G5ycneFDpNu0LBZ7eBfRpiSJSNc3aBOZljUYM1aQZHV9KacsncSK7JsRRbaX3+qDxJhXedzGLl/8Jgw8MwPfNsyE5ZWdis+frjnvep7fL3nraGAtq8e7D3gi5cel3gUhEI8H77EGJl52fnrMzH2HC3KrRcmgM1ftCCE5PLjGJ3/4R1k1p/yL3/gNzk41BztP8fb9h7x1b8FTpmB6/Sn0juXk+BSze41i/2OcLC3f+OAezo89MaQYy0sFP//zP8fe7pw//O8+xyde/zjWWK5PFX/6x/8Un/ncl/jYgeWlWwUvvvAiKngqI7Ay0jYrQkgoXfCVN+9hCs21gxml1Rhbcuf2LX70R3+EN77xDTZnF6SQwbvRhkHmEmFS3rHX1uYWBCKX2lpr6WNEKoUSkn50lX1yzPLzuRzv7e7Tao33jsIWQE7t5EZ7kkDIFS06m3FZYwguO/kSc4oh+khVZiM3ZCI5TxKCyhQEH3IFyu6cPgWc32SGDAE+MrR91iYow3qZU1ubVcNkMrs09oskjLWEmLj3cEnXr2nWd+ma+2hlEMlhbCJGRUgldb2LsXOE0BTVDqrcYx3XzOf7lIUmtW70DzofOzwrpFYUZUUi4v3AxdkjludnDH5AiNxN23l32fZgOt/j4DAzMUPf0Xf3aZsNzSYznCnGS1Z5XN1gTGMLPWG29xyb07dQMls6pJC7K4foiRGaTUcioo0dd+tk7VS/xgUwVqBkRKsZQhZ473h07x5d5/jKl7/CdF5y7douyGPu3gsoKYlJMtmfsFqv+fqXvsIb33qP6zevcfPWApQgekHf5jLkEDP4n9SaTbNmsXON1z/5abTKOp3BBd58+yGH12okBgT0wSOMJiRJPd2haTZseo/RBQoDMjGEgPM9WhraDoxRo2JDsKjv8PqLv8Az1z+R+/Y0K/p2xWZ5yvrijM1mzWRxgPceW9ZMptPLjtspJdzQMfRtZm1S1lR554CELSfE4Hjj8/+K3/0Xv8X7H9xnNp0ipWC1WYMy1CEx2d3H2OJx1ZUQkAJDu+bk/rucnTzCFBW3nn6Wos5OdBnM+nGTmsXsUlnE2OU+i68jt6+/xsvP/jRf+Oav0617zt5seOODNZGBW8/MufOxfW4+e5skPHhHu+5ZdQ1eDUz2IvuHGlxuKth7SeM2qMftd7+r+IEGKCoKbBIomSgkuJgYYh4AI7JAVspxcZEZtHgSgYTf5kVjGvUrj9M327p7UkKmODYRBPC5/wwgRO6eq5IAEQmhw3fnpLAi2IDXkqmVmP05n//qEq8rdqd3iCSa1RKGlpQS1XyBqiyMLp6u26BLDVIjpCWpcrwgLKT+kvpDqLwIyXFSTxLnEqEEZUpIkqQtR+2Ut5eKfqzvj1tdzYelJWxTOrn644k+MGTdySVhIMYapu2iEXNqZb1yWBnQSVGZRPRrUop4QRZCSk+MhtXKs7M3oY+Jk4sTmqEj+MhyuUYKzenJOfW0ZPdgymJnSlVpZtMDju+eUkqJxHNjOuPB2ZqTvqcPgUray7RcdlEVbLmOpORYCRNRQnGgWm6XkrdP4YduBKS+4LVFwecfrXn5YI4zjm85Ta/0h8Zo++eH9Enb+0RmlLZ3qG11ipCXO9vv1IDwsQFc5uCmkykvv/oKbd/y3/xXv8U3Pv95msGzeOXf5Xhl+c/+L1/i1mzg6b0JlZLEdJ2zu4b3/+CU49UZXitqm/UDQiSEyiDzy1/6PcIf5gW8UIYf/uFPc/eDd/nxT93mt37vX/GNN4/oVxXV5ILnbt9AzeD04h51XeKR9E3L57/0JoeH15mVE9KsJbQFejLj46+8ytNP3+Ho/sNREDp2je0kQufdv2wU2hhS15HG0m+pFM02ZRPDZZPF6PMuPFcYRKLIOg1jDFYbgsvpHd/3SE0uMTUGOdLkKeTrQ47NLQWa6Du8C6ixJFcbiyksSuWds9aGwmTWyancp0sGcE2D6xxMssfLMDi0sQxDjwqJQhs2zRLhA9LFsSpmFPCOVTQSQUoBFxXDkHCyRYsA0uBDoJ7tsX/9JapiTmEtTbvJnhfUBLfmYn2UNwHBs1yt8ENLWc+Y781xYWDoeurplL3Dm0hrOD9+wPnZMSl5bFEwO7zF00+/yo0bT2O0ypo2Es73ODfQtA2nJ484OXrA8uwBXXMBRGIcxjJlyXT3eYSdcXK+InQXVLWh7zuM0UglGJwe9UQOKSuk1GPRQtaDhRBgECQVEDKXOEsBfddld9Zh4I2vf4vp7HWEyKAnX8GCwLucPnzEu2+9RxivFyUtPsSx6nLc+Y8bkc2mp6r3eOXVT2G0oe8aVufH2eCtvsUDd/HY50pO6QZFCpYYVwyuwJgagWYIS0IIbDqHFJGgA0oVCDmg5Yzri0/xqRf/PLvTfYZuzenynGad2eLgczmyqeagNEnkVJjru8t5YLNZAtmwL6VuNMXLtyfTHZSEr//r3+G3P/MbLJcN0+kMpSTHZxdcrDdcu3WHmy+8yM2nn8Zak3VnUtGtz3njy3/A1778RX79M7/JW+/dpS4KPvn6S/zcz/8MP/Pn/jxaW6IfSDFcegzlDbF4PLeJbBr40rM/x5tvf543fv/3+exvvcPxRYfWktnE8sKze/zEz0buvHJIoXXuYzQxXJ/ucnp8Srcp8EIzmRY412cDQxGI/P9JiieODIMcraqVEBTk3iAiwuBznXa4pOdTdh8Uj1mTuK34SNtS3q3WYtzlEpEpIVMid6oc16GxlDmvXDnn6NolLpzTCU8TBJNC4buG4AOPNktOTpfs7E5o10tULPFFgZnPITiiz7oBKUXWl8SEljr3vmFAyIJcmpZyeRq5hHAsm2cYPO1yTTcIbt54Ch8NJ43lvdMp53566YOy1a5sP+fj7ENeULeui0LGJ5iWNP6bX/vJ8Q8jJVwVJYPvSUlQT2ZU9YxNs8Qi0FLS+0Cpa+Y7O5RVIqw71ucrvJOsVy2uDzjXI4Sg6wYulg2Pjk7Y3Vtw69aEWBlWzZp5jAzBc+vwgJP2IXlHMH5hyMsJIFf05G5K2iV6LZiHDc8fltw9v+DleYU1A8aBloHb85p3z1o+Nq95eDJwFHKPpjCC2rSlQVMaTe0eC6i9gCAiMiaigsLkhXCUb47n2kfFto+FvIvpgvunp9y8vs+m8fzOv/rXfOud9wjR0202PHh4irC3SMz5+t0TvvL2CiHIYExAZaYoW0OhgbfGyUpepkpsOeGZV57jU5/8FK+++nEmix1E8S42nPLTr+7z9LVbfOutt/mN//tn+fQnXuav/qWfZ7nqEVHQDT37z8y4vWP5+FPXmUZBvxkQukMWJXVVMpnMSORS/xhj/nv0OjFGXJ5kWueOq9uxyCXI2w1BPhmdcyQS1hpSjPRdR13Pxhb1HucyK7AV3G7bCCiTP28/dFg0usgAIFdQ5OqdLF7OO02t9Xg7i5Stsfg45FYTxtJ3HZvVBiVETnsoRYweQkAnSbdp0NNcrivlqOt4QlS9aTaklPLYyMRFc0pIGiMMi72bWDsnBtg9eIqhayGc4zpPP7Rs1mC1xJgaNwSadkUM0GxWaG1wfqBZLSnKKUl42nZDUVRYO+Hajdt4l5s0PvvCJ7lx82nms0OMqYjeEYfs1RG8I6XIbLpgsdjlzp1nGYae05MHnBw/Ynl+zPL8HOcC/XrNZnmKH4ZctdcNuGFsnBGyTkNgSFFQVrNLX5tRioNSCqMVRgmUtOSWIX60Pc9Gc8cPj7j/7gMKVdO2a0T0TBY7YDve+PpXadqeJDXWaPrBEYWCJFHKIITOYnnnMLbguRdfpaprNutzzk+PUeP3fe3GNc5XPdpk/6YQPP0gcK7l2kGN0B4hIzH2RBFwLqLlDNKAc2uMnbI3vc3Lt/8d7lx7HY1gffaQ5fkpm/VqtMLP56QyBqMyO9G1mwxqlWYickXZ9naKHiE1SilCTEymNYLIW1/+Ap/9f/1L1ssNw+C4WK7oh4G27ygnU566c4e9w/0x7Wdz2m9o+dJ/91l+7T/5P/DlN9/l/YfHDCEiSHzxzff4F7/9+/zHuuTf/Qt/EakLYnCEoScFh6RAiBKpdU7/jGvb7vwGe/IlPvfZ/xsfHG9wITP7F83AsvEsl4G/NP1xrr9Q4sJAHwOzqWKxN6XbOEorCSLg3Drb/3cdT+yK/43xAw9QPDIrg4VAlnkxQGrikEvOkohjSSJjfi+XcT5eMLLKPEcWdYW4TfWMqR1yLxpFQF2Oba4Q0cjco8b3dN2G1eaYqRfIbiAZyaOjhyQlWYaeL719j4/1uxxcr1FeYyWEfoMQng5JdaNECgtB4vt1NoOzZa7miJtx1+5BapLKSmgxpqpk9HRiznncYTit6aLloi9Y9cXIGg2QBDGKy5JJEvl4o/pTikxdZnMnLpG1QJMX/0t/WXJFkyCMF6SUkr7rSUKx2XTjzkCQIvTdgEMQo8vdZKOjmtQcHFzjwYNjjNEYNaHve9brNXU1wUpLNa3ZmS7o2yHrWBI4FMIqJlPNwd4ELTM2iaO6NY0VCNtrQKZEa1uKfo/n5wMr32HLllltEc4g0oBMPbdrGNrIB27FK3szmtPAaZF1NHF7Lgy5RM8qgxagyD9pmxYkm1xldsRvMeDl+fWk38kWnAghKIopzcN3+f3jL8Dnv85miNy8eYPdmeXs5IzdvYGzzUMu+iKXyds5Smm0zC6YyFxpksh+IUVh6brM0Bljeea555lMppwtl9x7eI8P7n3AerXmuadv8kNPX+eff+Zf8f7pwE/9xM9xY+G5OHrA8Qfv0ylNhcY4w02jCWxYO8VMHBKGLIArioKbt54ipJxClTJLsHNpqb68zvq+/1D1knNZyLcdj22JqtIa1/dABjSDyOmgXL6dJ3KtDdYWDM5fit610Wxb1xstqcqS1Xo9HjenG4rCjt+JyI0MU1Ymx5gdcH2Ml2XAruuy90dRZMtyKYk+0W9a+vWG5BNmNsFJRRzBT/D+8lpwLmtSuq4jBYFSmmJ2yAvPvEroHe3QkOLAenmCEJFCV7ihxzuXQcjQ4YdsHjY4R4qSwQ2sN03O5Q9TutaTkkfpEj/JG48QBDt7e9y4+Syz6Q4iaVYXZ3jb0jYrlOjRGtbtQERQTxeAHCt9DPP5DfYPbqOEYr1Zcnz0kOOH9xFkMBnHuVNIg3MSo/PCGkJColitVnl8dV5ahMrgIHoPyuKHLpd3S5hMSyaTAi0ERsDFvRPm5YZEYLNZszk9Z/CR1cU5tigQSjOZVcCAVtNxHsvGhmU1RQp49oUXqesJq4tT1uslcWg5PTuiKg3OdSipMDZgxNiHRkaMlQQMSgt6t8aokuAs3dAiREuMHfuza7z81J/nlWd+kkk1x/UbHn3wBieP7rNarXEhIVTuR+WdAyGopvPM5mnDYvcAqQXLi1NiTCid9Tl91zOZzfEhMp3OEcB73/wKn/3nv87ZyXlODxUFyktWTUNVlkShsNMZyuRUVe5DFXn/W1/hH/0f/3P+2z/8KudNk7srp6zBC8nx/vEZ//S/+C/5sZ/6CerJFKkMsrZEP24Uxk3odpOadRCCh++vOFr2+JRw0Y8VW5JlO3DvqOHkQc/OCwJVSFan5yhdoZSmnimm9Yz15oyLVcDj6Z3/jjq8Py5+oAHKICoGaS8b3qkISmZ3yaBH98aQC4kve6BAzveJ7CCbUyaXK0l2HE1pFC5tRQbjV5dGgygydJEColCoFLNzI4YuRsJYInzerOhl4M7z+zz46gPe3ywZ3mv4uLnBzAdMYdhsVv8f8v401tYszevEfmt6pz2d6Z473xtjRkRGZmUmWVFDQpGYoooqKLuhympbltwWsuySvyFAbSE+ISEhgYT4AlJj4waDoGyMMQ1UdwswQ1VlVVZlZFUOEZmRMd6485n38I5r8of17nMjodWdSG61SuxQZtw499xz9373u9d61vP8/78/8909RLnAdz1CKmLXQ7TETKXQPlWMm5wl+hocCFVCHxL7wCZMc9QTztwVnqwzQtQEF3HR47H4KNLYJz7TjjzTWGw3z1GxERLnYLv4CzH+3liwbIcb25NyJNI0NX1vsT5ya/8mMYzsiCBomg6RaQbR4qNHqYq6TacI6y1D6JnmU7TW5HmeFqehAxEJvqeazQDYtMntE4Sh6RqCswitCGo7O0/PKkTGFnHqfhE017MLJpXh7OKcz19Z0OaWnUYSVRJC57HlxVnJN84sas+gckMTwfU9tk8bx6Ze4q1jf2eXIjOUUpN9YrSTLkriPoToL6/t9t/b7o5S6vu6Bx/e+4BrxZR4fR8vBDIkp8iZjuxNM374puA4XuHv/coabyXaJNqu1hopNVGlJFfbbEDH8etjblEMfPett7hz9w4fffge3/j6b3LjxjW+8MUf4+L0lEk15eOjFU9OlvzR/9lneP2FOVJaDl79LB9+5x7dxYB9cA85N5STDWaa0dYbjMiRbUZmMq4cXsXkJYgVMUayLE/FCTEJR0OgKAr6TxQe24JkK6yEsevF5Rv5TJj8CXHsVrdzeR3HP6uVRomIUyKdAkNCEGityE1BjFBWBX0/IEYgmzFpNGG0Ss6JENFGJct0iFR5jtEJU6/GULRCG4wpcMKTacOgNGSKLM9pnb2k22qVBKqdOMerwOHhXUpTobxl45oxb0il/2VVeu4hEIOl73owCj8M4BXODiA0AoVRkRg8Tb1CtAqlFdPpeAJ3jiyrcH1PcIEin+CGgeg950cPQER2FzOEMPS9I8sr7Ajli1Gwulglx6OSHOxfgwC7ewfMFrsMHmx7nScPPiBETwgOH9LwO/pAP/TJMq7UKJodGSre4mwq2pAxjelcjylyJvOS55+7wZOPj7mzv8dMS+qmSeyZPEOZnIBmPql4ul4Ts4z9a4cIJVIhay0RmdJ8tWAx38EOLacnNVpGTk+PaDbHDMPAjet30HKgzJLV3A2p4+LsgIsCQqA0GVpk1G1P5xS52UXSszN9jR/51B/l6s6dRBm+OObk4fvce/cdWusZnMP5QFZUCARDP1BMJmQhjqO+JNzu+vZyHCVkT/SOvKyw1lFOpsQYuDh+zG/8y3/Bw48fMNjUBQ5dR912zKqCwQV8jPRuoOsahNzF2x5vW97+xjf47e9+wKobcCGB+EKIRBlJRWjk3XsPWZ4eoWVEmwKpM6QauyYxElwS0QrpidKxuljyb/7Vv6KzjmfwxSQg9iGwaXuOHm94ye7ShQ1KadrOMa0mGK2IYg1yIESPEpFpPqX9j6WD4o0m5hJlYjpNqkiepSp/uZKXM1Qx4tmD92kkBOhR7OW9T+OSmBwYfkyi9Yxun8sdBj7p4YiRkRS6TT2W9Drn5HyAesmLNyZJeFvlTDP46NEFt58/4MXbOzzpV5yvNpy7hvlsjpWBPHrUcUNVlAxNR7G3g5AzIqn9lsRM46nP9eA9vvYsL5YEN85HhcE5kdw6IY5AOkf0Cr+lpIqUbpyEh6k/wphdtPXNCsGl6DTh7tI/WyLlqFxMfSWfuk3Oj6I/Z5lOp6lD4NzY6k83fjHNaYeBYYhUkxnWBXb39lP+Sut5cP8+Ugm0EkxKg5QpbdfqZGOrqjmuGRiCIfc1SozU0TgWIhFAoSIQPVElHsOO07x0/YKjc8FziylSaqatxakWGSRhtBQa6ZjuHfKVxnJhLU2XKIvBOpp6Q2MbBPDo+Cm7OzsMJmMvr1L7Rqb7I4oUPrnVnlzeK2PIZOqceLbunmGwHJ+dURV7XDnYwVqHjjBbLJiezHl6vuar996jXLT0zpMHS6YyjJLjiMJAJtksz3H1kmovdQQ+me/z7jvf4fzkKbP5hJ2dOdmdQ77xrd9kmsP+tVfZf+4aH9x/iycff5MXbryBKHN6vcv01iH5cxW7eyX500ec/85X2X/lOn53h+gGjLMIIrdv32Gxs8vy9IQtR2ZrFc6zpPW4dKCNRZkai4xtFyl1ewyEZ5qdLM8Y7HDJk9kWKs6l4jYKmbqN430mRCTPMpCMlmdNnhtMphl6h1YSUeR0TU+R5WitGGQHIQHagvejeyKNooahJ1Op2GN0qVR5Qd8OCJns7sSINhqTZbR1yhTaFlBCChZ7BVrNiI3B92uaepVScl1q1UspaOslSgucb2naGiFhd/cmQ20Zmg7nQSnQxoAe4xlCGLsXgb5rKDoDQuGHnuPjp0yripu3X0rWztV56gVHWG96FvOKzEzp25p605MXBW6IXCzXDHZgOp1x4p+mkbNRWNsT/MDtuy8yX+zx5OE79M0pbZ8iA3o70LYdqpDsX9khDiC8HzeltKBk1QypBCEMqCKi84BUkqs3r2Bax9wI6tU51kMXoHeehTZIMdC3azICL372OW5/+gZSS1CBwXVIoSmKAq0Nikhfn1FWOf2wIos1sYBFVqI8KZIBS7A5fVvjW4m1gbww6DAgbcB7jXQS3zp8tWE+f5Eff/XnOZxfo2tWNOsLzo4e8uDDDxg8iY7sPUIlYrGQGlMUSGVomxadFwgVGIYeKTUmy8exTCR4B1KhtGHoW5r1Be994+u89513afvkTLu+vwcxUuQ5Tddyerqk7R3rNiH9g/dYO+Btz9vfe4+T1Tp9/kP4Pt1cjMmhdXax4uHHDzm8eTd1XkO4tJhveUXJEKJAat7+2q/yzW9/BxciPibmVSBeCsJd8Fycrukbz2AsUqv02VAarSp8CBhTMJ8OEBVD75A/eH3yu7tAcUAwEV1FylylVq6KBJtGEC5GlEkiJYRHSJc21pAyIGKE6B2Q3BcyykRXDBJn1bPTXSQ5RUZrcggeEUcBJKnlJaKhnBywqa5x0XUcbxpUaMgzxW+/d8abH17w7YdLzpbXef25HXamGq8cNec82SxZ5DnXr1yhuHpIPpmhq5KUQrUNXOvTqEdn4Adi1+CawND1uGFAqIBWu8k27NMN6QmEMFbGqMuR1uXRNEIcra0xjovquKAExgIPkW7eMSEgkDomaSMSY/Q6COXHcYjHZBKwY1vY4aMfN60AaISM1Ks1m03L6ck5Z2cX7C4WDNYxzStyI1ES7DBge0/tW5z1aK0p8oKn5xdcf3GHdech9PiQJ1HxKLQ0XiEU2Bgpo+OzU8eF1RzoyH7ugCTWStA9gfCWoBVntuStzZLlUBK8Q8aBru9o6hohJNf3r1DNpqzrDS4GTjYrTJRURTk2b1KCUZRbV86/EygYt3h7iNEjpUngJgfv1UsmdYeUgnqoOW9OCB6GZsUwDHRxgtmfIs9OyPIyFd1SYQrB2fqUZnNG5ockENdpTCHhMtPDO8vR0yc09ZIsk3idMZ8ViGyOsE+RtufDD8759J0T8t07/MvffMTxt95ExCWLWy/y8z//U7z8xo9x/5v/muvXDphVc0TvYPAsijKlgPuYLL1agZRIH1HeI0Ig2gEZkz0xOpfGMTGCT04eESJaSGxIHAipVLLJiq1kKj7T1YgketZapzEoInVRR6fbMDhkpjEji6Y0GcJDphSDtxBS5oskoGW6UpOywmqFtY7CZPTdQGcDIhuvn00hawnZL4gG0IpAchBKMepgnKYsy22rFrdJ+pWhvSD4hvXmnKKaUxSL5GqzPVU1ZRhqrO0Q0mFUxfr4Is37o0SIZNVWZDg/oLbCxhgQwSFEcprk5ZRNfcHp6RGT8i7eJm1c12/S6EmkYqNta0KQdG2Ncz1d07Bcrjg5O2V3Zx839JyeHFFVMzrbIbUkL5LgOc8Lbt5+DW+PcK7FjdllfVOjK83eTYWWOe3SJxAe6b3UxRwrOrSMIFOnWDpDoQLdvac4IZFFSXAg1BRb13x4cpIcT0oxmUy48fIu11+ZpANh8LjxcGQUEDp675nvGLS0VGLCDjkxTAgO6o8Ec9Vx5CRVmTMrJ4QdCGJgs14xWIeLKaWZZs2gPNfmX+DHP/MLzPMpy5MHbFZLzk6esrxYYtF44fGjjRph0VmO1iLBQp3D5AV6dJw5O2BMfsk46bsGoQ1ZJej7nq5r2Jyf87Xf+C2enq3xIXLnxgJr+6Qpalref3zM8aZhfnDIwZXDdP94S2s72vWKd9//iN6mzLatsF98ohqIwOlqzX/9z/4bXvnMZyjHMQ+jmDltGunPRyRRejprMWWJOz3Hx1GAHsYxepJKstkMKFcQZIvWEiE62n5NjNkop3BpBOotSn7C+vgDPH5XFyjIdKoPSmIVaVQjIlYGfCVAaAabKJoOQdSjbsNvZRcBVBqTxNCnL0o5tnQDQo2n0eBHC2WyX3nvRvFHGhPJGIlBoWZXKQ6f5+SjUx595z1sfUSz7vnWu8f0HdjW8+tfecDHbz/ihTsLXr4152BXsahKnn/5Fa7euIs+2EFNJ5BXoNLJLcYextsmQlLJh46+79L83BgCEaV8ghFFMQZMpcsUiJdVdIyBsG0zju6R1GFKbb64FZmOJ+FLsmrc+vSTxiCOP9OHRJXcvzKlbyw7FFy9diXN8rVBmpxyOsOLEZwVRHLtXKw4Pjqh7xyZMqwvarQWrC7WSBHRQtA0DQAmsxAFZVnQdgNWSy6WUyazHHshxy7ZSGIlqeGt1KhoeUE1TCaC9bnk2mEEOrZwNEEkSEnmBdZO+NrRwEc2ojOH61rqugYEVZGnDSFE/GCZlBXrpqZuWp4Mnuev3xo7SumCZyYbuyXbou8ZpnqrTxFiSwiW7MwyhDZ8dP8jhMq4vnOFG7slq6OPWOgVk9df4B99IDCTXWTTE9EoKVAR1qsz2uU50fnRED+ORPgEC2YcgzjrOD9bAveZ7ezQXSju3z/nW++vqG3O6cc92Xs1p8tfZ3jrnDemFimWbN7/Fr/0fz3hT//n/3v0/nX6k1PmewVqUhK9Zzab8sLzz/Hh994lxm2a80jTRVzSYu2QCiglExCMCGrk/qSDQ7h0QDHeY9sxkBACYwx931OWJUPfJjIqiuCSYL0oKparC7x35LpI2TASirxAipS43HVpFKFUumeUkgkqV1YIkmYnyxJ3xGiTiqSQ7v+t2FIoleIvxs5QKpzGUFExjqAiWOs4ebJkZ0eyvnhAnmXE6KjXp8xmC8rpAY1PXCTnO4zWCYdvSjbLJnVzBan7GyNEiyAF/qXumUJYT4yWod8QQ6CtV3z4/gdcO7yJtw2MTBOlMqwbCDFQNxu8TyRfpQ1tW2OtJcsMPgy07RprLW27wo4F5M7enGAc6/WaalJRzXYwZKzaDdmkYL7IyERB7hUxKJxeX3Zt5tMraDOncy14i48WIxJKYPX4Q54sWx4tG65UgpfuPo8vDlGrDYMbWK9OOasHDvM58/wqE3dtvL8tjb8gCkPJglm5oLcNzoNWFc72KO1wscMJyzocc++iRagcIzus0oioMVmLVoJMKnof6boV03nBzVuv88rNn6UUmouTx1ycHXFxdkbTdljrsdZSb9a0bYOUCpUVIFPqdFYUCJIOJYSAyRJOwvt2DB5NZK2qKum7hoF0AP7ut7/No0fHFLmhzHJi8BydrXE+cLyqOd209C4wnU6ZTyd4b9lsLFpKNk3NyfkKvwVvRlBbw8NlzEGgHyx9TJ+jNBozCKkhBqTSI3k6jei87XnuuTv8n/6P/xl//x/+Mr/9rW/hfUCqLcIgae42XUcEqskOUkS64ZRMFqkzH0UK0OxrBJEh/kdUoEyqSFlJjN66K8YEVy0IE0HbRtoBnB2nF2lfxmiBUoGilOSZZmgk7UZhh4TqJnjkCHMjkISlcRSWIlNrWUoIPiUOI1KHRhmimTCYfd4/+ZCiddycGH7f8zOKkEY0PljuHkxYzAoWewXz/Qm7+/tMD69gi4Jstkc0aYMR3hO1IjCM3v3RTeO3BchoUQ2REC0hJBaBC/6yzR/DWFRtUbeMxQeMHZRxQ5NJqxPjNttoa4uFkMq7sajbhgKkTlLwCWp0cd7TNS3WuVRFq6TPaTtL3fToUuFdxHtSmqyPaKEQJrVZJdCsa4rSMIwis9l0h+VyiR261MKVHucdnbc8Odpw9bqHuEivadQ0pEsUIQ7shTV39wuOznte3gkYA9Hr9KbG0ZckIUbDqSv4Xr2kjQOzoSM0luhswpKPxV5pcvxg2VxcUPcdQUTWQ4cnoGMSmAmZLLRqi1SHcawjLrsAW/R9jFCUBftXP831Gzd5//33kULw4N5bHD1uub6/y/mmo5ORZd0hhjOCBIdFeIcdGtbNKbG3CCT9qKXajkISHyJ8ovuQunF28Jw+PmZSFfzen/3jsNfTbQp+dPca06PvYi6O+ZnDyGvmgrBecqZnPO1afuu9h7xx60Xq976O+dT15GoZsdl3795mPp/hfUDFZ0K7T4qCsyyj73uM0ZcF2r9rv/6kkPaTv/fJjJcsyzA6S1ofHygLgxCe3gVccEl7tp1zh0jTNOMIqbgs1rQ242HDXwqW+6GnLKvL52KtpZqMo86xCBFC0rUtusovx1OfTKdW43vfbGq89/TdhmbdEcNAXQ+pyxiSMN/anhiSe82HHms3VNWCMLJapNxmMo0dN9IhTCkBIibOE2n82bQN6+WSzaam6y2LnT2EhKZZAoKua5JjhIATjs1mQ5YZSj0lMzlStcznc+qmBgJZnhGCIzM5OtPkWYVWGT40dONo6KP3P6TvLfsHVxj6nuAtWVaSZwapJSYrGXpLWayx9pzz83O0lhiTkecZtu+wy5aZiexMcvZ3JkzKgmMf6T2U1YymWeHwNPWG995+wONHqyQkVpAVBo9iaB9gdIVSCVwnZUnfp7FUnhsOD69BVGSmJOIQIsfZmt47QtOkjqMPOB9xXjAxN3nl5h8iD5qzk0ccPXnI2fFxGs35wND3dF2PkDIh6seDa992ZGUFUo3hf3J0xUBw/vJeiwiKaoK1o9VXCJx1fOft77JuW3KtsSHy0dFxGjV6z+DTaMX7ZLkvioyh7/EyYEVa/3Z25qm79onPURjZWWnMHLh5+xY//TM/hZAyBSJaNxojFEL0mJHQnEZEKRvqh37odRZ7+/yXf+cf8OZvf4Nu6NJ+K5P605OAmENXszPdQ+hdQrAMtiNKRYw28WNinsbf/7FoUKopzCYCoRlnE+kUK0Qkk1Dm4G1KWA0+puhsIZAykClJmQvySSQzKTI72IzNRjN0khh6QnQIF8ZeWRjFYal/IKJLFmWXFgspUlutnM6Z7uxx6/kX4NTxyvXAteKA+nzJajOwvIhI5ZlMIvNFxd61q+xeu8rkcJ9ssUDI1LaTskdk2SjGlUQ8cbQdCx8ITZ8SQPGEKJFo8Da10IPHJbDEpQAuxDS2SYFUsMXZpzrEJ/ZJ1ElQjMT5ZCGOYovOHgXF47WPpOInuDjmS3iyvGI21ezOdyCIkWlhqes1vvZkqsBZwciLI8SAtY4YEtQNFRmGjsFZ5jtzhBFcuX4FF5ILRChJHBzOCR6fLzm4Vj1j2iVC24gYd5Te8+JuRmsH9pWmrAKDVaiokWKg7iVCw2SiWdrAvSFwHgbWzQXR5DihEMpTxp7SKIIssLGl6yOruubkfEnAURZFckBIBSKSqLIpwO5ZgZKemxoD1aKICKFH5nHkw4fHPD2qmZaRK1evYVfP8f/4p/+c1pTIcsJkr8PagHDniOiZy5w2nnPafUxcBCwSXSbsPF28tPL2fc8W3ATg3NalYgg41n3PRdPzyme+gB72uLI+58AsqJ474PjRI+SjIyYUrEyByXMIA7nSrHvLYAdKJdK9ajIODvbGbJxn+SNCpNTXMFqOtw8pJcaoS13KNncnyzKcS6h1Y8z3bfzbTkVy8kh8DKm7QSRET1SetregFYU2lEWZNCXRj/Zkh5Sa2WxG3w3fl+MTQhjb8A6rLdaHJKYVkhi3yH2BHV/LNqhvm6yrtcZ7nzJQ5NYFlyz7bljTwDiWG1k9wTP0HV1WpM1BSMpiQZaXeC+xQ9IoGZ0n8X1wlyOdbHQyAeMGlwqrpqmxbmC9XlLkOdNJOZKMoetavHdkmaFrWtywYdU07O3uIYhs6hVKS4KPdF2ikeZ5QVVVFOWUvJiM3aIMMaTDgxCQV7scHX3IZtMiCMkaHtdkRuO8HwFkksxIijxjGAbWI/RMSsiF4JrRzG4esr+p2Znk9DHw/kfvsdrUlFqwU+VkUmKU4OzoKUenZ8kibjRSBOrWJdeXkOPoKwlC66YlhsjhlUNe/dQPcXy8oemWZDrQC4OMFik8fYwM/ZA2zRipyhvcvfGTaJfx5NE9VssLVsslgw9E1HaHSQ4aBEPfXSZIK50nGCYCqXRi83jPYFMq/fZ+RyY95LMCHM5OjlmdX9B0NmEq+hWbvmd3OkUrRdN3rJsWleUcHDwLXbXRYZRBacOLz93m1772O/R90m1JkpZSybQeTWdT/tOf/zmuX99ns1khhELnBXlepnvN9ZdpzUJqYojk5YSdfUU13+EX/w9/gl/6pX/AV77y63R9SoJWQjCrMorcg3a0/Vn6u6VEm1RUtcM5VZ4jvEFVk+87hPwPPX5XFyhlBrlO44BxK0AQ0REy7Qm7glMDT1aSi4YkaorpFB9kJDaeUgh2Momapc7BfZ1xulZgNV3XYNV4IvMyYYnH6jI5f9IIyIuYPrQqxYUbo9k5uEbjVvRqg5oHru3mZCc1k1lFVSj2D3Y4vHbIZHdBNauoZjNUpsB3SBVhDI26PKVJAcEigiUMLcE5+r7HeYhkKedVV1inGILExcS/TWC1NMpJU/ftqSzh2SUjp2LrgCHB3DwpbCxcdkxGgfDotNh2YLa2yp3FBKE0sQ9Mi9nYUgfvWxaLkr53XJw2LC8aEIF8krHY2wUv6JsebTQhOhAZeZmDjNRdTSUrkLB3OKcsczabFe2yI2Sap0drdvNdlBg9OyEQZWSImudMw56Zcr5suHUjwzaC87ZBFYbFjsFqRbGbEXRkcJqnDwJd1+LaHisNuVHsScWLeztc3Zny9oeP+fb5msZFzlfNyNgYUdLjNU6AuzHYMYrvW4CE4HKzTUW0TH9eaZoN3Hmh5NahoR4GHlycYbTjledmvFlPWKkZRZ6TZwJp16yOHpG97uj3Zzz2A6acI7VCB8ur3x6+DxWfxiyf6BoEAShq34MWfPTkCc/NLZMo2Dx4QLko+d4m5+LK59iIHHNq+bjrOWsb3njlFv33voq6zL1xZONoo2mbUfyaTlVap0LY2gE7Fh3PipTvT5neCmS3Rciz018Sg25fDyLZV50PNM4yiJByYDJJFwdsSBRQbQx5nuMGy3rToLP0s6uqIssKnF1+gtNiCCEwDDYVDtaCGPNovL8cAYmRw5Jm+vGyONkyP2KMWGtTgvAIbJNC4FyP0XnSUw3jRjp2b9zQM7ieLCsB6Np+7GxUKdMoCJxLBViIw+iqy3EuDfOctzif8l2QAe97slwynZVsLo6TvmYcteV5TvARqQpUFihD0qKtlxep0zZ0NE2HtY7d3R2USKM6rRXBOZwNSOkZ+p7FzgEhWGazmqIo6bqeqiq2fVWUydFZ0q81bY/WBcZkNN2AFONowFtilFS5pI2G2wdztArc3wyslsdsNh1mPievJNXUsGl6ZPAEadIYLjdIIbGuAyTB9WR5kd4jMTq82CL/04dQSk2ZFxA8rbcIkQ5iRuYokeGC4PrBD7PIrvD04cc8uX+f87NTmralqKZjcZne560wGyFo6waVZVSmwA1JODz0KdFXG3Mp+duyYXKdjSGJyRWDgPOzYx4dn3Gx6dmO34ssw3nP2bqmG+xYBGmKMicSsXZAEvFCgYTPff7TfPn+fX7z699g6FoKJRGiYDGfkpUTfuTHfoQ3vvAaXdcw2DEHSKjt6Q6Q9M0abTLycorKckyEvJjQ9x2feX3C/+5/+79ht5D8ztff5KwdkJnm+u0MK1ZIkXAfg9tQFjlF0VJveoL11K5JY1FRcKk9+AEev6sLlDsqsiMDJorRHpwWPG/TqcRHT5EpZovAE+lohzESXEuMAOmhXUaqKjLPI0pBLmFWSA5nBhUrLvrAaeNYtwPeurSlq4gIChE8IjhicLiYtnspoK+XKByTas6mazhvAwe3d1muevavJrKeR6BnBZPdKZPZBKkVIQwI1yNzQ1QliGf44hg8wnfp73M9XdviXKKzRgJDPzAYjcynZKpAxBRp70NSMonoiaMjJ2zHGzHpIy57TzGOdrNkMx6bLJfdgDT73Y56Yhpx2ZTZ4QZP7zvq0w6jy8sF2rotDyK1D2fzCmsblFZ0TY+3ka5u8dYlrcL4fGOEYAMXp0uUgc36nN3dBXlu2N2dUJgZddOxazRRpgJBSUEUkVIOvLhnqM/PuX44J8829DJjb2eGWuSILLCQOeen92hWFpvvIV2O6iyu7Tlpaw6nE567PuOLL96i0IHzpw/4rQ+OOelTUnb0Fq3zy411m6Ydxo7AdpP9pEh2uyknSqpAGEmUFd3mgrm+YCebM/SB8w/f5Bd+z3WcXPFbpwqppslNhCSuW1TX09FzsRN40gum2qKjI4tp4KJMCuFzLrmQMqUuBc1SSKSC3KQi9un9hxTmO0yrjun5MetNz94Lr/FrHzzA9ZJpPuHYBX7uf/kz3HYt3/zmt7nzxddRUtKWBu0EBMF7H37EarVM+gRdIYFhGIiBkZ2RupTep9Zwbwc88TLbZQuXg7SQX1xckGUZ1tpUCIzjmECk7loCAhfi2FGAwfYEwHmH1AkBP7h0ct2Ccobe0XeblJtjDEJLTJ4RQ7K/uuCIJM0KSOyQ7JFSSozUFKYgGENR5Ak6JgTepaDCfuiph46p0TgCnRuIQJYXhODShhUCzg5keUFuDOvVGXXTsLd/FR8sp2dHVNV8HEVFnLP01pHp9FmKIaZrKiQxWJztAD+Cv9p0Hfqe6XRCWU2T9Vclka1zFoHGhyRyz/M83YtZzmazpO96mrZhUk2ZTneRMmL7lq7tsC6FGipt8H6g3pxTllOKYsp0Noewwig1jj3EGB/i0DpLTpO0VNA0A4vFdOz8BrR32KFl6D2ZEYSgGDCUWYacK7RWSCUZ2g3RJ0Cfkgo7OIpqgjEG1VlyKWj9AGMxK2XSOQXvqesVT54+xAdPNdmhLHO6/iTFBYhAqTO0ytByj0lxixeu/BAnD+7x5OFjludnrJcrssmEvm0TKdWYNL7x/nKUIrWirCqGvkNn1dgxTZ1nO/SErRB8PMT2Q5fcPCNELYbI0dNj1s2Aj6MSQQgGn2y8vbX4CFok16Rz4ZlbzttxJOOZlIY/9BOfpz5/ykf3HnKlMsyqgrsvPc/tVz7Dcy++SFEUWOsRPqLz0S7uE9NIjfyeFOqpCcFhsgnSFEipUbrj9c98GvzP89yVCdkVjV1smO3k9ChkABdapLLUfcvCFMyrOdDStJYin6TXvL1AP8Djd3WBMvWBiU/xzz4oBg9DEHRB0tjIYFOisVSBvUIR8pSjILcZ9ELgPPQhcNKCUYJKCVAp22WaaaKSNCEShaC1GuuB4AkxRcEr3xOiTdkvfiCqDIGkq5fM5xVhlVM7x9GyQRjD/v4ck2t2dhfMZlOMDCgiPgyoYNIN6wX0A7EwCLWdcfZEO8DQ4QZH1/bUdccQNCafj1ZZgah2mFDio6aPKdzKhjjOsMdR1OjoGZ3Gl3oWH1ImBxEkCQYXRtFfKpK2gLfRxhpcyumJcH56zrqroSsoignpJ+fUa0e9tkiV4cZZZJbnOFKx2LZdOqH1faKEKpXsjVERxnRWFTXBezQZwUbWfc3x0LOfZzD1SKlhLA7yGPj8rKPpFEWl2S8H9CynC9DYY5qnF4isAJvRr48YbGAjL7D9FTI/kElFaxuGjWViFNOZQUTB1b1dXtk75+LBmo1Pi4WSgiLP2Wp7IFlP5TgH3rpPtpCxZzbbJHr00VN3jhs3Jty9ronrwLUrM67oAffoHv+vjxSTw9+LKTRaapSwoHoyMePpewN713POVEceesosZzNYfPBpEwOqyYSubVHiE/ZmEfDego0I7yl2BnZUza5YU56csBs6rAz8Z1/6ES5251jbcDhfsL855aO//39HffgeIjqyqYHDHVShCcowW+xQFnkat4SkKWrbNp32pWCwQ9LDMI72fGIKDSMa3FuHd+m5b7sSbZtgc86mzkXTtiAl1qfo9+A8Sgus7SnKgqIon0GwSHEWUmm0MsQgyU0BCLxNG7Qj4EiajvSPpCgKkAIXPVE+S18WMfE+fBi7KkqnUYNSGG1wISAUKCNQmUQZMQp7C/A9XdcmbZKR1M0S2/c0dUOW52gZOV/VZHkJUeNcsqUKIciMRpASnlMYpSW4iPU9AsswDCOQa8C5ZPe/fu06k+kiwfPcQBBpI+pti3OJYGpt6ugUxRTbDzTdhslkljQ4Po2NnY9IkTauLfW17xr6vmd5cZ46QYAySW9RFMUnNqCUcZNI1zKNSCLYPtFop6UhZ8AOjtOLc4pcYUPk4TJibYeUsFpu2DFTIokLNFhHVCSdk0zxBtoYnHNoZWiaDm00k0k5aoNS8btZL8kLSe96Yt8ksTA+dd+VQ0tDZXa5ufMKw3LJg4/v0Q8JclbNdwjRs6kv0Fl+aXHXeQ4x0NRt6uQMPc55impOiGHMcWOkWY96QECLkX5L6kAKElF5tUpi2BASD8eHFJUAjJ+b1Ok2RpNlBmd7gtbjeh3GYrJm6NZMypyDRcWd/SlXrx/ywud+mIObdymrEpRKdF+RtFBd04xQwtRR8nYAAtYOSStUWLJihlQGozOMkXzujR8nlx6nHvHQvsN8skvT1zStQ2lFnhuiH8ewOjCdarQqknvMW2L8jwR1f9oJXCkhyjFpcnTsRLBe0LvI4AU+6LRgjQI5JZK0R+mIEqQAIyEYO3YYJbBRcdqnTWhRwuF0Qu8G6i6yGQK9C/RW4rxGRAvBItEYdZPF1Zd4ePaYpvFkasrTdcOTR/d57toOatOQdZFqUdG5GtdJhFHITWrNZ3mOYkroNuAtKrMgDRGH7ztoNvSto+kHuq7DekHnBVVZIrMSaTQKRQyS4LYCpmQnFiLZw7YahRCSeDaOs3HGjV5EEFKRyhS19asikFg3hi6Oau8gDCFEzi9qpFQUWlGVBTFKhExwtRglPkbIJEPTszmvUaQkZeFD0sr4gLcDQ+/Is5wQBdPphKqq0JXGeoftHc2mIQRPPpmjdUEQYiSCekBxM7cssimn6yW3bleoSrJsTlluztnUPaI0hDri6iecNANHZz357i7rbCCbKPZ9jy5LMtdSKcfGJqDVtatzfq96ifv1e6yXLaWA1/YrioMr2GKP2G3Q3mFGrL13NlmZpSKMBck2LTtxUATRBQg9u4uc3rZcPLhAdYbPvvYS//ifv8eFPmSuMvIsRxnFUF+wPjvianabnd5w9r2Pufv5GU/jBoPCugEAKRVVVQEwnc5SsvAo3twKU2UsuHZ1hzd+/HO09cALN3d5SI8b1pSP17z3Tz9C377NIlecN09Zf/d97PETuq7h6YPHHL//EXfCL6B/748SZjl/8Mtf5lu/8VU+/Pgjuq5LBNVRWN4NPW3XpLEPgaZtcD6ddsWIrbdiGAXEYdSomNENE8nzDGUMSvWpsAlhdEMInAs4R3KDZSkcsRsszqVrXBQFmc4IvkOIZFPOMoPSioDE2x6l07hAIZiUJVlV0HvLRVsjTSpEhBBjLEbiC211M5CcSGoc2SkJSkYyo4CIs12yOlczhr7BWo+zA5t1TZ5VOO8YrCfLSybVHGctdbNKz0cXGB1xrh1Pug5Eeq0hWJApS0wgaOqB4KEoCnZ2FimnKwraoaNvW0QMmBE5r7VhCA6CZ6iXabzlPW5oiHk+Fgh6FKZHBpdyytqmoW1WKJWIvDpkxGhTp0QJQnSYshjHv5G8KAl9P3aOHFqNRTIpMbfdrPAuueEGq7GuR/QR6Qc2tcWISFsnMm2Wl/RB4nuLVnLs/ibujNGKoA1tbPE+FbdKJuI0UTC4gSzmDEMS8frQJ7u2yBPhWAzcufUpDvLrfPThdxlsoO8tIYKzA02zQeo0CrRDn7QlYzcseI8NlhAjpqhSyvZgkTLRg1PXeStCTc/XuwE5OuyU0gy9Zb1uGMai3Y2ifGctSiYrO1JgnaNuW5bLJYO15GaczotkmrBdgx86rh3uMc01d64tOLjxPFdv3cVk+fj5H3EHIdC1LTLGRA0nXmpHgneJDCwFxTAwQZDl0+TyDJ5JNePVL3yJe/e+Srn5iBCg7UGIkkkpMUXCQvS2Sc/fS4rc0HVdwnj8x8JBeRwEy96AFSkkjJhm7NtT/ui+GWKkjwIXkhAsjkKqaEFGRo5BaoUqETAyksvkr48h4dJ9CBglmJYaoSJFgMEFBufwXkLQxBjovKXzHqTGW0unSnR+gOh7jlctT8+PqIynbjquHu6wf2WHpg1My5Isz7C5JZ8JdJ6BbQn9gMynSCNwQ093fsHFpqfuAuv1BiUlpS7QImctr7LxBofAuoj1McWyjw0jJEhE8uIznu6RqRiJcbQYi1FjA0oohBfJFYRLTIYokCO63Y9cFpMZXnjxOdq6pVCGamKQI6HVBY/Sirbd4J2nWTXYpsON76FRmugdRTGlrltm8wV5mWPyce7vPdGlNnvbtRAEIiaNg5IGokcQsFKz79e8tih4tDzmh67kLCrBufScLs9ZLk/Aa85P1gzAJJvxzQeCo0FzVe9QTRa8dNVwcC2yWa8oImR2zdPvfYOqKrm6V3FlX7OYV+zXHT/x3FU+ffcGXzkTPCUyLXcp7IAJDT5q1HhCSpHHz645pDEEISXwSiL96Yq+OMW7DeF8yrCpebp2LK5NadslE3cVUVTMFjuIk0OkMBTeM3lwhfKq4OiK48PNGn0+IGIC3G1n5MYYRIzf15lwzuNsAB/4zV/7Cs5HvnjzMyxevMrxt5/wcoAbZcYHH79Fv25YP/yQw6i48ekX6Sc5p+/eZ/rkmKe//G+oXn+FcDDn8OCA6zdv8PTkmBA8WZYYCEobssIwnU4QQlDXGzKj0ZlkGAaKLCMzhtXFEimTxiTLMubz+Zhe7BJHZBgYrKXr+1EnlQrBoXdpfCUEXRiSoy6mDpaWijLPRwHrMxdQBFAyWVFNYtE4AUJJkCIlg3vPNnNyy1/Ztr/tYEdacLwUBCOAINLoAEXXpc5GjA5yQxSCEEMSDIdkZy7KhASv25Ysy3F+QEhF1zUQBZlxiKKkrs8T38doYlSjuFcSXeroaGVoa4tUmkmVMylnKCHx/ZrKNUzikMTbPozOtJKZjMRZCSbDS0nUeepkxUiQJo3lxnvY2oG+H3j69BEhCIqySp0saxPeX2ZpHK2SXkZJk8adPoyaE58E91HgQ0TrDKM8Q7vmeLlCCs/xasOkNOgYkcMGYT3aKDaNI0Q4PDhAZzkRRZYblNZkWc7aLS8dcelQBX3v0jhTa5x3NE3PbJpT6EjwG4yE6ARD34Kq0HIH4QXnR485PT5JXd2Q3tuh7zBZMbJXksg8aZoUQoAdLG5Io8SmPWce5ehSG3H/QqSxmhfoMbxQKIWJ+VhYwDD0rOomHeIYR0MurRc+xMuxmRsZV8cnp6xXaybFLoKQipO+pm+XeGt5+bVPIf2AloLF9RcweYHUMlHHxw5u4nkFondE7xEikBclmcnp+0Qzlkom2QSCag5SZXiXxnK7V25z03+W048+4MHxPbrBMyskMThOTmtCECCz1LnM0hpdZBpn439QnvHv6gJFVpLBgB0iuNGtEsSI+E2ZOz4K/Ajw8iEm/cCoj5CpwL4MEyQkma0SMIjIRCTg12AjXQc2pBtKjCfDIEBoTaYVW8NVhmV37ypNtcPq6XtY31HmGfuLW0jdU8U1DGfcf7Ti6LTm1nqgLBTTSUWWZeRlyWS6QUvDYrGDKR2xqRFSsz494/T4MV0XqTeOznaYwjAVEVPOaeWCIZjkwkEQhAc5akZi0gIMLo423PSiY4wYPd4I2y+POTzeu9GBES9tyyKOC7yUMBJ4Y4wcHx9zenzCp55/CTOmayqlxja/YzIpscOAjJGhbZNjwjqGfuv4UNhocbgkSB4SEr0fBvpNn8SSaIQHbRLcyOokSAsiouPAczs5UfQc5AWLPc3SL1mtG1ZNx0kt6G2g2t3ng4ePCf0J/TDlh27ewVYVb330EermARMzcLLqmJSG5UXP9dIQ5ZTvLAuePDljL9e8/nvv8ukbOzxucz566yPCJNLmu+SZZl5knDeBqUz4bekDijEK4ZKaOuIthMRZS79c0poz3v3wYw6qGd/68Am92WfoHYI1vjlDT6fUqyXt6iFrtwHpKPI9uu94dosZj/uW3ZMG6RaX1uLxDR4hg88CC4UAmTlMblD5jOPlipUcuPrSTdpvv40YGlRRsXfnBdz9I+TpY57/4mcxswJtOyZvvMTpr30LPn5M++gx2d3rFMbwmc//EO+9/z6r1XpssctkOfWRLNMMQ2ofP3M6iSRozfIxvE8nXcGo4UnFlEsFgpRkJo0UfPTY6EcI1mi3RyPVGMwo5Ihbl7Rdz850Op6stw6j9GdW63XKKkEwLUo2fYcackLf0nUdWjwLJTSjw2jb5YnjJnIpRBYKERQyZkSvcF06FXRdgw+GLC9SB0drlNO0bUuoA1UxTfRcP465QocUkrZNnJeu32B9R4yKEB3RywRfJOnSYhRY7/AOdhZzdue7zITDPX6PrmvpV6uE3Q+eYC0+ejAZIgiy2Q5td4IKEVOVGKPJhSCvJmmkWpW0TcduaRh8zvz6NQafOq5u6IghMj9YsGoTkr3tkiswy0ucC2glU8QGo+atTx0UrUF7y9PzFct1gwuOwXs2bdIRrdohkal9kucrpcimCyzPgh5DSOOsLM9St1I+c8kIATrLgKRn2mw6Dg9mbJoGafoU5BnTODQ6x2x2iBzg6NFj6nqDHRxDP3bWlGLwSZuklCIrCkyWEq3jyMiqJhPqTU1AYYpiHDNKtgA0rU0yZ9geHzyZyUZrX3LBDYPlYrXBOo9W205Lco7FsTByPoy2XmiahrppgZ302faOoa9TwZ2XXLl6g6FdIlWR8oCUJDg33rcAfixUSAGSziJiwFuPnGt0VtC3DYMd0ig1gjAFUqaimxiYzPfZPbjFlYvP8ODoCaXx9N7SrzyDyyiKgkk5g+CIrsZLyLN5onyPQukf5PG7ukDJDWQF1D7QxYj1cqQyCHxMszYtodKp6OgDdCGmvJMMijKh8c0nQHpb4ej2FBZCBAMq0wT7LN9AhIhBpS6DSCd6SSDPFTfN51C55q1//RT7+CF0kqOuZGcxo5IyhdShsJ3lne89Yj7NKPOU9jmfTSjKHGVy8qJkMilAapyPDE3LerUiy3KWF2ukVkyqK8lbnu8wCIONEhdH1olQI6RtxBNvhbARxiUDiIjg0xhnROBvYVpKSiwJxnbJjIij6weJEIEYXbpGUROCJjMTMlOlFrPSiYK4qWmzUQcjFTtXduk3Pc562rYnBiirjMlMM1tMsJ2l2wz03TC2/EVizNg4Iu8rsumEqiyJQuIFzMIaKad4obkQjrUssd05djin2ww8OrJ02ZRHT5Z8+ERybQqvvLRDuS85Oj2nbmsKsc/N/T1ONpF3H5/wxVevc/z0KbJXfO3BU1558SY/85kXWMxLrJP8t7/6DvdPzpl4wWQacYOm7zI23cDhwYQ7t/eYFCrN9ANAoorKKIiey8yW6dUZ9z94h3/6D/+//MEvvMSvvH+Gu/0i03zB0CyJ9Sndesqj97/LbPOQVVjR54qJP6d6mKFWA3cXjv2THnk4Q8uki9g6YoKzaTZc5HRtNwr7PK21NLXndNNw7/Qp0+vPsbq2z9OPN1S1Z88Z3v/gKbuzCWInxw01RVODhukkww+W5nSF7C1KZLzyyqssdnZw1jP0dhw37HB6dkRmDNYOZMYgBdjx3pHREyP4aBAqI4QeESRmHAuYoJnInCaTRN+jhEE6RZ4lV1II6eRcFCXzxYw8z1jM5ly7eggIwtCzmM04Oz2nbROV8/7DhwQh2d8/ZLleUtcrvDBY6Vl3LZvVKr1nPqT0b56Jt4MLafShTUpQV2nTiDGipCHPSjKdo8aY8bbrRuhZizZmTGLOoFJ0Tc3F+dk4etXMZzHBIYnsLuYIJHW/GrXqiQKrtYGQDg1bW21XD7x0+xbPXz+gkJH2+DHLi1WitYqA7TuEFPRtl3Jj3JqirMiFpFuvUzdotSIKQZFlmOyM6ByqyBFCkmlNVuTsa4nIszSGiDF1VLMMO/EIbbBMaD2sh8h57SiqCtU7YvBjirxGCYHEw8UZm6YnxEjTe5p+AAGLaYlWin4Y8D5SZZrFfMGVGzfpokYoQ5UrZrMpRTVFy8QY0aYlL5IIMzNpNOh86uJmeYL2uWiZKI31Ffgl87IAMlSUhMGzWSdA3dD1DP2AUGlTtyEgdIbJK5RIdUdwDtt3yQK8WdN3A8Vkhh0sZVkhlcTZ4TI0MXjHliKdRihjWCYCO/S0XT+KbsXYFUwd7bRsjD7K8dB4dHzCo0ePuX3jACMj3lucTXlh5eyALCsIvkdn08tRUozhUmsYQiDYnpT0vR2ZprDRtm1RWhOFJCAZBo9oWnw8RRqDMaP2ZrNif7bP3vw6FZoXbv84VmpOV4+w7pSms6ggyU2JyirW/UOiKxl8y7Nw3v/hx+/qAsXEyDQTFFrSVZHGJgFV9AI7kD7ERPJxlo1IyntiJDNgDBgjUHqc5cn0PXEk4CUjLqN+ALCCYCE4gXIRGRNy2wsAlzoVJmNe7lGEFwif+wm+vT7j/ORD8vaCfnOKFJ6pDswKj+88fdMRfYcsFN4PyPUp+f4usSyxruJso9g0HetNzc7ODkaXBD8wycFkGZO8oFgcUGdXqdUkpRyPwYCSNJ4KISAZsf0h4r+Pdh/wkMA7PLN9fvKhlRpHYCPbJCbASkzikbTAakFeFug8iQyjiCCzFFYWI65JQmKpBBfnS7pmqxNIepZ6uSESMCrHhYB1lhDimIQ6oGXGMLZYB9tja8/CFCAcMWoQJee247g/RXGFnXrNNHrOLk4IruJgf863jgTnQ8FLn32FRXvM8zf2CcFxWBk2Ozs8Ol9xpxz44u//Ej+xuMJ7v/mvuHntEK9Lbi0iP/7ZFzmc5VycLfknv/J1vvK9p8SosUMghAEpInZIEK57D1dcLM94+e5NruzOidGCsPigEKixuINcBOL6jP1pxR/87HO8de8pq37GLNtL+ikJdX2CMQU7RYXkKmpHUN8o6OqWTb0mmkhRO0TdJz3O+PZtM2+iUqlToAwudAlPrucElXN6/gBlJI+PH3Hn9h1mr73G0/ef8tJFT5Y/gaEhBEnRCSwjjXnTIjpPP8s4LHdQ2QRrLbuLHRb7u1ysVvgYUToJVDOdo4QiWJ8YGKjUrYzp8xm8w0cxOh0KpO8wrmfwo3tEGpRIC+lsMufmlVu8/rnX+cIXfw/We/KyoJxUzIuSIssoy5I8z8dTrxzHJh3D4Oi6gW9985t853tvM58vuP/wY77x7Tc5vXiCDZI4SMqqYnV6lpwO0qCVwQjBxOR0csCoPJFDvR95FwqjA1IEhPD0fYP3FgHM5/vIGHFhIMSYsoWkTDZ8oTnfnKGVRBuDDwEfLEWZrJiZMuPo2qCNocwqYrQ4mUZbRI+3joNJwaeuTNGhp920CcU+DGgtsTHQNy3OB1wIZGP+Sr1a0tR1oo0qjaqmKKXoho7VeYK16cwgpMIIgSorjEosFpnnCXceA36V+C8mM5jMMMlyDiaa5w4OyA9uEkSWro95hk2oL075rV/+iN4lW3dyvknawfHorGZrH9RKEoXi7p3n+Mmf/tlEOh0F+0YnZILtB5TRZCYnpIFHOnARsC4QgiO4ngcP3mFeZkgNs2mBdy1EjVB7LKrbDMuG5ekxXe8IPqbRTp4ThMSYHJVPKacLXNcQfNILKinoh5EborY2dDXCACMyL1DGEL3D9ckm7FxPpQ3W9WhlCDHifFo7tqMqpSRSpIRpGVJHcBto632g7wYePz2lHyzSCIK1qZMSBeV0J+1zOsfkFQiJ9/YysTjE1E31bqDvGpQ2aJODVIQIfT9A3ydb9qi9cj5imxZrE2OnrCqaZs2u65nNrrA7v0E/nLI3ew7mU6bVbVS4xunyA1p/RM+HKJPTuZpoutTF+wEfv6sLlEWEXVJ0d5tBk8sklA1bhoIYke0QokK4iIEEO3OR2EWGBpSIKA1ZJlEGBsBGkU5P1qfQHx8RwSFjRI+tlhC2qTSpH6EFKBVREuZXdyk//wXOj+7RD0v688fUqxoimL0pRih8P7A7y7l+MOXWwYTDgx2KIiNl40DjbOIBKMFiPmVnZ4YxBWVRjFRIyWTnAKY36A9eYpbtkQWBdantrNSWN7HF3EeG3rNZWfo+AdI0CiNBRDc2VpIGxfUJROeTPivlnaiRNhM/aUsGEMgIVaGZlMk+uTw/wTo1zqk9EsXQemrb0jVpDh1CT5ZlrFYrnB0oZhXn6wtiCEwnE8qixDlHKRNrIctUWjwGCyGkk0YALyJBRQrTMrQrHp4OBNXxfKXxQbMJkcd1z6MTgxcD9cnHfPruIa5v0BJMDJRG8t7TJTvK88d/HPIZPHfrkEdvvcnenuSPvPE6i6Ik1ud8890P+M33H9F7SRQR27W0mwvKckJZzZIFNArOVy3ffucjrh0e8MKdG2g1WqgJY1heIpT6rOLJxw0377zM0XQX816PmUzJVUlTS2R3gjl7QD67RtRXmE4U7tORh85w/DgyqVsOTxz70xIlRQqGlGkUEkMabWZCjz00AyJjb//q6L4NGAWbzQVBwf4rL3H6tbe4WH7Mld5wOJvTnJ5w8uEjDm/uEzrJ6qQllFOGvRnl9YORN2HI9nd56dVPcf/BA9CCKFMeVDGZYvKSySxi/QXVZIbcONZ+oI4LjDLIzKGMIjMGl5UIbdjNFTeeO2Dn1l12Vcatqze488prXH3+eaRNM3KlFF3fk+WpGM6UItcqIbVCgjaWZY6RJUwk3kWu/sE/wJe//CWE0nzw4QdY1/KNb7WcX9RMqwm4QCIrhbETuBUYjwLMEZa2pdJuxcd+y2wRUJUlSimm0woZI/3Q07se6wR2sEgk8/kOu4vdNNrKc6JP1uiqqIgEnLcok+4TrQ3T+TQ5SHSGURm+6yiCZy4Dtl7TDAOb1YbOOaTSLC/WtG1L36dxRpZlhDxDaZ0Enn2P1IZsVjJ06UStjUZPKob1iqHtk+POOeh6VFZQVDn2+ARtDNJk2K5NtNRNJAqJLgpwltliQxwa5tduMVlcRZmx2+k83dHHnJ+dJdfKuGm6rWA/xhEXkJg6WgqqMiM3mrwoCDFcUoBjhFm5SGuSiGN4axLLg6LMIlqla/nksSbTFUF24HqkNwzBU+X73Nr7LI8evMXq9JQgsxT4lxdkeU4QAh8E+pL9kwTnMsvwbmDoWkTwyQlTqvF5RLRKo0qtDYPt8MEhAmRZMcISwfsBgRq1LWNjmjhyndI668JIZRap22qdp+u6pENZb8h2JjjX0fd10veYLHWsdIHJCxJzSSHVloskiX5IMQyn5/gA12/cTJgLEQi+H9f21AAQMpHMT04v6NqGq1cPUyyCybB9S1UumMynLLt3uXj6Ie+d3ePHXvxD7JT7SGOZmgUn5xf4WDMtDjlbOS5PUD/A43d3geI79lyLEBGPpo+aJkrOlWQjoIzgbWo5DmGLe4csE6gJCckeE2U2EpNlNoD1IXVPZLINSi0QozUr/V1jvomPiKhHBTTjTSZwITAAxe3nee33/TRKC+5941fRJ/cZhsQDyfKMLC95+fk9rh/k3Ly2y7WbV1G5BhT9umG1WYO8wFQBqTOyoiTFZqdclkyA2bnN+uA1hsUuykSqAMTkIAAuxVBERYwSpUXKoNAOa30KS4wplIyx8h+Bs0hIpyeZTuLeOXqpsD6ObTpJJFE0l+crpIAiK/DWs16u+eDjR6xWq8Sz6C0+BOq6SW15tsF5gswYjBQc3L6KIHD+5Ije9cyLOQbDxdkF89kOm3WLKXPc0ILRGJ1DTN2aNgqcElyZSaZ9zkRl9G1AGcX5w0DdCq4u1mSyIstaFnHNLJ+AgE3s0XgW05y37p3w/K//BvtZpF8N7FYzbLvk/PH3OF+e8fHJmn/ylW+xDkXKvgke39ZIGYneQnBMJnNElDjn6ULk/sMj+sHz0vOHTHKVXFLjDBohkfMD/MufY9XX/OTPfoZv/Bf/FSGrENKQZxW2jkTXo/SKSM/01LB3rji4OmV1e8piteK1heAVc4X3HtVYHF6kdq5WCWmdkXKUvFCovGLT99TNEpUJnE9ZT3lV0cec3Tc+w/G/vk9FYHH7GhdtzcPzFV30aOvZyRfMbl5h7/OvEKcZ0TnyPEfqjC9+8Qt89MEHVGXJ7s4et27c4vd8/ous1ive+vZb3L17l2vXrvHk6Cn/4ivf5rsPWgZzhYM71/nM73uReWgQR8c8efu30Q/f542dG+xdf4Hrb3wBf7jHWdsj8UnoGFJisTaazGiCkVRVgQ+OoevS6BWN7WAYUv6Rkho7DGRF2qhffelT/J//1H/Ot97+bf7BP/jHfPjBR0QVk7hTK/K8uLxPU/YPdF13mdYc4zOmiJQyXQch0yhPSnbnC5YXS2bzHfa0RskkOpRSkeVZ6k6OCP3tzxcyEXm3y7gaLeJ+jK0QMmH4u+Njcudw7YZ2taHpOvquY3AeH6Dpe7rB0g9D6kaQDi9pPYiUeZ7WOl2nA4yUlFWJ7TtUNcEuVwTfIFSG9wOqt9Tri6TNGBwYR3QDod5AiJi8IjiPcA4bBTMfGdoVq7Mj9q7eRU92sdby9N4H1E2THIVoVOCy4BBCJFF/hCgCZVZwcHj98horlTZ0730KZbw8gMUkUFbj5j4aHxLQbMyi8qPFP/REEdFGUuTg+5r1+Tmb5Qo9mWMySfQxaemExuTlyLOJKRBQQu8sROj7FiFTNpZW6d9y1N9pYwje4r0buyNJ/5fWXIGPgSyvyDJFmWd4n8ZtyASdHFyy/iIERqlLbWXX93x8/yEffPQx09dexNuerqkZBpuEwkJgsmK8DpIofLp/vBvFv4GHDx/zT375X3G+WvN7Pvsqr73yAi++9PLIdGJ0eAa8dfz6v/kqX/v6W+zu7vLH/pOf5cBomnrD6vyE/et3WcwVTbDcX92jaTrOm2/xaPkxvT8n13PW64wri1tcr17n/PTrl7C+H+Txu7pAOW4jvhjrZRmQDGnDNJL79z5iMZkz2dlFFZKSrbsp2QWFDGiTugwxQggyzRcFKD9mmTybDKX5IIzL/DbL5hn6fWwkpPFQSKTaDMOLX/gCuiqZzUuOfvuf0yzPmEwqdndm5Dim0xlagh0cLkSUyRBCo40nMw5pWrSMuAguSKRMCnapcmRe0e++RLN4HqdzpPCXrcD09NLzlCPePoSIyQRyBlmhcFYmfkzrcP3YevKpmpcKjJCYEWeMgCAlJm47VAIXBI6AEoJqTNi9df0OB3uH7M5nXL1xm6998+v0cWBaZfS5omk2DI0lRoWUYqRkOpROwsCqyLl58yaDT1yMtu2ZVBPsMCRBWd8n+1s+2uYIEAV9lJxag9q5zsYoXpgYcn3BB8eSOzdmTNunCLXH6mJgdz7l6hTOL45Z9z3ldJ88U9h1z5NQ8Kvvrvhff+EOWp2TZRU6B0vgW+/e45+++QErm/QyiDEyPXqGriXPMtpmQ/COqppSZvno3Ig8OTqha5b80KdfpirM+P6kE75kwM0znnvuFW5fe4ls8ps4XST2ibmg9U9Z7E74Az9+i7t3r6NcjlpYyms5M6mYaZjgGTYt/8U//BVccEgjE+o+ClQWsbEneMngB27evsW7997n7PwJ00ohBSwvarRQ9Jnn4AuvsD76Hi6P2KJATRQnD+5xtlrDRcPnP3WNg9vPw8svMFQZyjtiC6IwvHDnDn/s534OEUAJjVaaKwcHSCW5c/1qal9LyeGNA269/EP8yteP+K9+9THv1DdoP17wJ372Db64L8hOf4Lv/Zd/h/lvfxP3Ox9w0XZMfvJLZFUBrkPnZgye7FIP09tkxbXplD70PVIpoldoCTrNL5FRkCmB61qG2BORzKYTfuyHf4xXX/4sv/HrX+X+/fvcu3ePi4sLTFmSF4l1I0UaZWwDAbfuIDmyLmB0/EjFZrMhhGTRzfKCqpownU5RJDtoMdtFCJW6JCpJ7EMMyRUTkrBSqnSPxRBQ0ZArNZ47Ik72nF28h1Serm6pR6DXum7xLnUkVl1P03QolTZAG91lunVuDNZ5ojEEB835GV5KZs6jiGRK4n0AlTRMCdtvETLxhqJQuK5HRp+4JIAXAzJEhrYhxkDTNGRawsMnHH98n53rz2PtwJN7H2GdS26msSsQL4v21JUVSGSMFHnF3ZdfRWcpR0oKmWzeOo3WtuMgIVRKmmYbJ5AKdGOyxEkSApNLMjNPNmO3xjuH6wzRRPpmg3MDwnmCq+kHl7KFpnOmsxKlBCbLCJ4kdrV21JUojEqsGqVlAk5WCYnvbI/RJol2jSF4j5Ipm8e51L3aBr4ZnXYXKSQueAbrRgsxY3GZYiO21+fs7Jxvv/0uB7tTpkWk6wY8SZibCiGVNEhK4Ts7mhzAO8tms+ZXvvIm33rnQ6zzHJ1e0Pcdd+7cIcvLkUWVPMxD3/Jbv/0W3/zuB8ynU1599UV+ZP4FbFmyXp0z37vCdPoS9dFXOV+u6YbAabOiGy6YTBxt35K5q3zn3e/R3H3AqhaEcP0H3uP/gwuUf/tv/y1/+S//Zd58800eP37MP/pH/4g/9sf+2Cc2xcif//N/nr/xN/4G5+fn/OiP/ih/7a/9NV5//fXL7+n7nj/zZ/4Mf//v/33atuUnf/In+et//a9z69at/6DnsskKhClg7GCEEBBRcNLWvPU7v8qN/atce/5l9PQKhzslWTbOQdPt9OwHCZLXmLHgGC0tglFLcflN6dfqcsifRE7bHNtIGocgPnFhC8OdT3+a6wcT7heOd3/nVwnWU1U50226sjYIrQlRQTCjylmBUOisoNs0I/AqEqUmKxYIJdDTA+rZXbpskp4+CsQzAdInQwEDafSkpSDqSCzSjeyJ2L4gDhCtZ7CBrh0IW+2ISDqV7WgicWJSw0UHgQ/p53Rti4/pOUolcNbz5MkDHj19QBSCum0ps4LrNw55+uSY2XzCfF6hFKxWhvWqpz4942IYmE6nSKlx1mNdINOKsshZzFKLux16hq5JtjlAIhExclorpKzYqMj75zVffKFk5g64e/MVPrj3dU5XgUkG/eocOb/L84cl+XzC6SD43skpp+sebxveedpxmk15/oZluthno3f5b37ru/zyb73H2WBZ7O/hEHi7FfGmbgkx4v1AHz1BCIoYUFqhpKJtalxn+M47H/L5z70ydlEgRo8XCg3EznN8es5QLxH+PYKesFNu+F/9iT/Oj/7YZ9idVEjXE4cNdA24HhE8wWpkFIhxdmyDZfCWqARDtGS5wgWLNDnXbl3jZHlC3S1BWWxw5CKn72wiJVeBPi+4+sbnyH1LHR3mzj7XXjyk/fgR8njNeRDsxyQUFNGT9wMRTxCRTAnuXLtOW3e0dUuWGU6ePKCqKnxfg1JkZcX6tGGuSr70yh0+OlK8ubzCO8eK//e/fpe7/+mnePFTt/jcH/9f8PEHH7Hz6ILmq99geuMW5aef5wkbXChSOrAPNOs1k7LCGEWztmMhbiA4CKRZu9Jpc3EppiACwUvyakJ0kbZrKXTBz/2R/zlCSQZrefrkMe9++CG/+mu/AiJpZLQ2l44j65LtuOu7S+y9EKl4jqMF+fT4hK7vmNRTvPdMyoLBBvLR3SJFQh+sl+ecnp8ihKCpG8qqZD5fJCT/6B7aO7hCnqWQxJOP7zHNBHa1YbNpqDc1m7bHOUtQmqbtWTUtXTdQ5gWZSSNppQQ6M4lO6gKBDmQDUmCkpj6/IDMKr9U4y5XY3qKVRpMEvCiBbe0l7E4pjQ0e3/cIlzRp602LVpK1dbgAedVycVETh46L84tRNA7W+TEjSpAZRe9c0mEIyI3m7nPPc+u5uyilx7FoREiTIgdiiuxQMnWhnLPJpRLjJfNDjJ3tGCO9a8lyhZETQlAQOwRhBOppMpMl8vb4M/puQFYkhojOLrk3MSRMvTYZs9ki4QLEJmUCkdYAW7dkZi8VydETth03kqjV226MUxjQRjOb5BiV3FlbXleyTSdictKPxBFrERii5e3vvs/+7pTD3RzXrqkmM3yIFGWBUhpTVNi2Hp06aT0fhp5HDx/z9jsfInVGmSkGN7Dc9PiYgnBDFGRZnvYfJwgYTJ6MGw8fHtF9pqPIWzarCzYXJ8yq51HyVW7dNOw0T6nbyNvffZ/PfnaHo6MTcjKUWHOyPqWXuwzuf0RQW13XfO5zn+NP/Ik/wS/8wi/8e7//l/7SX+Kv/JW/wt/6W3+LT33qU/yFv/AX+Kmf+ineeecdZrMZAH/yT/5J/sk/+Sf80i/9Evv7+/zpP/2n+bmf+znefPPNZ7kbP8AjyyEvRln16I/1EU6PTmnOz1nVDUbAUC3hxjWuXj8kM4ZnA5D0eFaCiE+UIduSRPx7v07FyCd/gnz2cz5h8Y4REBGTafTVO+y9/geYPD5F1o+pKk9pFEIFhDaYYoq1Htm0GJ0xDJ7eS8jmkCl86CizElAoAUWeUe5eYTU5SB+WsTAK2+cYU9GWnkMaxsSx4JIhPa+tNkcRwAhEIdFeUlQK25lx9AW4Md0ybCMG0yN53VM0r85KSiPY2dlFKUVdb5jNZuztXeH0/ALvch6fXjCZ5Fy9tkffD9R1O6rtYb6o2N2bMQyWtu3pWod1LiGyewtBYLK0yAJkWfYs3kCkToTzgtO1x4uco0Lwvc2aqpoxxBU7u7voytMOkbOn9/n4oxUvvZpzdiz4tXee8jsPG6yP7OqBjRP8f371a/zYZ15m/f77vPN4xdfur+i8x+ic6Wgr3FpM07UItG17me3iwwYxblja6EQ3tY4H9x9x8/oBt26kWS5CkGg0kvVZx9npGTvTGX/wp16nb2u+/BM/w9XDKdgad3aEDCLZIJenxHZNsDVBSET0DM2aGBw6M7gYidLRi55WOIyaMMunvPj6q3z1za/gWQEDIWQMDLR+SeOWTEUFlYTnbrA8O6VenTOUOZPJ1TTqLE5Zfu8hrl3i1yu4qDELQ8gifd1TmJx+vcEOA7lRED1FnmOUYjGbsVwt0UpSVQaTTelXA68/N+H0JKfvDvjOo3u8/7Dl5cM509duk//4lwj/suewmNLc+5D87hUmuzus2oYqL5nMpyyqadKueIdzFqX1ZRib1JKtzz4rcpz1NBcJ7a6UIcsMq2USsMpM0fYdk9kMGQPXb9/iyrVrTCdz1usNj3bv8c2vfTWtBIIUzTDqBrbdVIkAHyiyPG26eU5ZTYlsYXmGxd4cZXKCc8QYWK9XrOsEcYvBUxSJipuPllVtDEanoEOlFOvTU7A9tl6xvFiyai1N02JDpO16fBxo25a66yEEfIwM1mN0WvlC35ONERHWuQQw0ybhzGW6Hzsv0JmgXTdU0ylBSNbrVaJnm6RnKssyjX3tkBKsCcmFJQQ+QN+nsbrzkcEFgg30fUfddCOBV6ZwOikSrDGk1UWKFFuhteLFl15iOqvSGjMe6FKKerhcka0dkCGt0BJJFOJS6O+sZdtVefTkFJ2fc/f6YeLceMWk2MHbAaFUIsXaxJYJEVReUOZZskt7y9B1ZFme2D5j6rWUihAcZVkQlCH4YUxf1yPYL9mAx9kdMfjx+Xica8EJpITFYoIUSWBvlELEmOy9ctsVAXxAiET1dd5xenbOb/zWN7iyN2OSK156MU9dJqVRJsMPDUOb3rMYIxenJ3zt69/id97+kNNVQ54XaCmYTSa8+NLLzBYHKKU4PzkCZZgtFhSTOZ/97Gf5+OETEIKv/s7biBj46Z/6CcqioKmX7M93sH3Npl0REZyfX9A0jqYRHJ00DKtv4W3LnjMUxSc2yB/g8R9coPzsz/4sP/uzP/vf+XsxRv7qX/2r/Lk/9+f4+Z//eQD+9t/+21y9epW/9/f+Hr/4i7/Icrnkb/7Nv8nf+Tt/hz/0h/4QAH/37/5dbt++zb/4F/+CP/yH//C/93P7vqfv+8v/Xq1WQFoMJGLseqXqOggopjP2bz7P1Fvs0NJzxDtff4fyC29w9c6LIJL2ZJtncPn8x01++yXxiV9/32PsnPx7r3/8v21HJXUsBVJEvNKUN1+huPt5xCNJjBcMrmZS5qlylTmbdUNbb1DS0HeexqUCwg2OGAQ2CnItKXPFweEescxJScQ2nSaICJVaiJcx96T1WcWIcC06WJ5NgQQyeoSIrKQhCg2ZZig0fpaPryVlFhG3Dp/ta01I/r6RKC3Jq2zEJKdrppVK1sZuYHXR0jcd3gWWyw1Zpxk6j7UhJbcSiTQondJqjTZj7PeWvioT5demuShKYMZAuC1C2uMQUTGEFNh4EiO3Jte4ezCna95HZYrDHYO3kdAteFyf8/4TzdHas1oHXruRMyng+mTCymU8XTpOxYJf/fiIjx9cIKNDSsM0n6IQqUMxXoztybnv+xEIJgCHHdIpKc8VTbchkxOc9Xz7229zZX/BZRCedNhQ8O69DV/97e/x8vO3+Ok/8BrTPJ3afHOGu1gRzp7w8IN3WC4vGNqetulYng1EkaGVIM8kfZyx6s6x1hF0JGpwTmAk7C12+PijD6iKjIGSddePbggYnGNTN9y4pnF4ZKaI84rCRIb1GdW0oj4+wQWfQttaCxcthZV07YAyJc4HZBxo2jZdkyx9RuqmoaySPmE6X7CuG5yDawcHmL7mxefnvCc87zw4YXJ9yvvnNSu7YD7f4fqPfZHTex8S1hv8eY3sE7+lECkYrqmbJB60FqlT5yOXMrXjQ9psnLcQJUVMWo+iLCnzAj/O9BEJbLe7uzvaQ/vUgREpZO+NH3kD7yNvf/ObnDx5yOOH91BBID0UOoXE+c5SFSXeeS6aJc5ZsiznlVdfJ8RIvdmQZWm57bseEyHLDDJoprMZi53FuACNaxCCPDNpbBE91jkgMZ6as6eo9oyma2isS1bUPAdraQZHcCFp7mJExcTfcAIEnlLl5FmWOo9KoFxgWqRxT9M27OwuEpFYQ7usIQo2yyUp8TmJrmdVBc7TbDZYaxE+pNemJFJn1HWNUIp8OmFoB6z3iOhp6Vg17SdStRPbRW9Fxt6hZBrB51oxqSY8/+LzSDEKZ8OAs0nTkeUlJs+TJqhtR7s5o+YjoLOUk7XtlDsX+c7bF+wdzphWDVWR1hhtBK5taOoNfd+z2gwopcmrKglOvcP2LUIlsq7tW4auIQabMoWEoO9atNG4mCzDUidxb/CO4B3amOQK9Q5vu0tNU/AO5wZMVnL1cIfFNKfukyg3KkUM8TKLLelnxOVoMcaE/3/w6IiL5ZrdxYT57v6YpgzOdth2zdCtiCiENDx5esRXfuvbfPz0DOsikZ7MGD732Zf58pd/P1leEkNkurPPZDLhxs3bmKzg53/+F/jWW9/jw48/xlp467sf8OqnnmN/b8FmecJ894DjBy1f+eY3eOX1W9Trjps35jx9UjMrckyleHqSc3htyqTw3H/4P1Ga8YcffsiTJ0/46Z/+6cuv5XnOl7/8Zb7yla/wi7/4i7z55ptYa7/ve27cuMFnPvMZvvKVr/x3Fih/8S/+Rf78n//z/97XB++5//QJu4vpmI0hWXaB4/sPmRYZ/bLG9y1NlKzuvUWpOg6v30UaQ4jpxUvSurA1pMT4zJXD1rIpnlmOYTyQwTgu2v5q+5vpX2ErUhXp10JGilnF5OA2J48/YBg2ZDayt6NYr9c411MWEnykaVqM0fRpXcX1HoEmz0vK3LC7W1KVOV0cePLhm5z4nL2Dq6BLpoe3k85VpsyTuO4xqyPy9pxd01CZFNqWGEIKET1Sw3ntWS4vmN58juPsgN5MQBmiULBFS49n/bB9YREkCqUkRZWxuWjJ8zyVjcIwqaY46z8RbgUxSurNkLJGlKKzPbbpyAuNt1C7OrXuY7xMAs0yiZAORKSqilTIGJNcKsQE/wnJty9jJAqLizk3XvhhDmYZD79zgvIrVmfn9M2KTBe8/OoBRTXjNSN5/CCST9as6p5hs2Z3J8O1Dnt6jxdv3+LD+8eo6NBR0HUdTddinUOMluxPpgf3fRJBEwNd8BR5wWazoR3Bct55rFd8cO8hzz93B0HAeYdykbO2JCsq/sCP3eHsnTcx1+eo7CCFkNHzwXsf8uvfvIcvC65dv8Lec3f41q+9ze+8/SjZdMWal3+oQs0EwQq8dSnhdQV7V/aZTSq++c13iNISSWFkuD6RT11gU7dY66hjQOHRVYaQgcpNUEpz885d1jbSHW2ILiBOG9Syp9M6va5hYHCOajLBOYfzns1mg3OBdd3Sti2z2ZSyqlhvapYXJxSFYBIb7h7mfKqqKA6vczw84dQJDgvD9NMvsfrca/Db3yW3Ar9qyJTCzOYMQ09VVgzDgFAyETeBfkgJykVREAMolWEHOzrBCjKTp1Ru7wlInHV0XRIaJgaEQA+a+U5yiBiddCafeu01To4e8/Wv/SYyphiIaTEBF3F96vi1XQq+3NR1OiBITVWkhGVBarHX/Yam3pAphcwMk9l87HQEsqJEkroCWZYRQ6Afejabhsyk4n3YLHG2Z9N0KWOqG2iahr7tCBGGGBlsQISI8x6jJHpsTGcykaD9yHmJwVE3HVFmmCLj/OyC+e6Uet3ge0tUCjESZq1LVFex2ZCbjE1dk2lFptKo13lPtJ62t1jfMZGpk+W8R0vBputw1tE7mwCXuabuO4hjfAagpUSrpPfZ31lwdZ6j+hWtcyBTTMFmc0EIC/I8Ac/Kqrp0xnRtQ9+1yOjwNolDt+ntWmqiHTg73jBUU4oiY5gKcpHGdGen5/ReMZlNsNYRvRu70AGJwPYdbb2mbzZoKRIkzjukkORFgWv6JEa1A3YsQJItWhKCJeJHA4ImBs/Q10kQ7QaqKmc+L+jPLdGBDOnYlRKXkx3b+aT3CSGhHmIIDM5zbh1t13H92tVn6AbrcEObDAVCI1VMBd5Y6BotkUpzeHDAz/3Rn2KxmCe+l5bM5gv29nfZ29sjKyr29vf5Iz/zU/xf/m9/K+H2u57l8oK2WVKvIsFbdvcLfviNW7hwSFUFMrNmGCpuXp+y2dSY4hzpLZtNuHS//SCP/78WKE+ePAHg6tWr3/f1q1evcu/evcvvybKM3d3df+97tn/+33382T/7Z/lTf+pPXf73arXi9u3bPD7+mG989R/jNyd4UfLcq5+haWdQL7En7zPdmbN3+BJuckB7/AFDfYbrNrhYcnF2zu3Da0klzZYBshXCpsKjc45AINeG7BNjnMAzQde2aPnk6OOZeHbb9gWPRBjB9OAq9+QU7QqgpPOKZtWzqluu7M9Zna25WC7Z25+hdYY0SZkugidTgUmVU0xLMBbRX/A7/+y/5cS2vHp7l+krv5di/vMsLzYcTPfoPn5A/fQ++XDBwniyStJow6wyaB2xQ+LATCvFoRTs5i1Z/RHV5jFntsCpCT6b0ekpLs9xWiOyEh3GwkxJoko2wbpuaNt+DMfa2pvT/5xzSCRK6TF5E8pyyu3nn+Pk6RFPPr6PFGLUCQScHdHkAfIsBVRBYqJMpxV9N+BdQsgTQYwk2lRISmQm+fSnX+W5F14lay+4c+d1Tj/4LU5OzwjBkpUFSiiGoaPQJEthMWPzqGeSF2TSsjOVPGwa5gtDrgTeJ2l0Oa3QWUZcp6Lrci49dlPS6SZeiie11gQb0snPJlhTIONbb7/D7Zs3EDKivOCkNbz5/kf80HW4OT3GrdZ89PW3mcwPmd99jQ/efsBb7x/xwufe4LlbN1kc7NLLOf/iN+4xOXiOjpwo1ggtqPYmBODs6TnDEHjuudfIpeH+g3sELIPrRqeaR8tICI7z9TkPnzzm069+mlyNsQ1DT5YZssWC+uycWZGT7e2irl2hP+mplmvc42PUXpk+FUrSdAlU5YaBsqpQWrNa1Vjn6QdL6ZNeqShKnPdMioIFkd3NwMt3ZjwwsGwynjSWF0uDXJTE69fov/E+kxCxp0vmXtDKiO0tnejIy5JhG24mEisCwPpkrfU2JeumRO7ERHFDR15W9F1HN1iklHRNg5CC6XyOFCmHxRhD0/VoU1JMKt748S/x9re+yYfvfJe662i6ngVpE3HOpQ0gJBt5cqjJlMQrkqNiOpmys7OT9BQxtezbtiXL87SZjdo3azUmzxJ9V2imsymTakqwPTJa6vWG1bpmXfdYqamtw/mAD3HsGHlEBBsi5bgGGanSSGWwSJPcTNZ71q3FmJASj/OMi4s1tuvTmEMk/VfbtQiSTsTaEe+uNGF0B/SdTUnSRuOix4ZIkIrJpGJ5eoGPEW+ToNx5D2GMXHA+sVhiErtvQ9QBXn39dWZXr+NCIERLvV6zu3fAbLFHQLBarsgyg8kymvaCopwglSAvy3E9T7o5KRV5Zvjhzz9P0y5xNvDk0RHP3bhFV3dMigVeSi5WNT4otMnJspKL01PKSclmtURIzdC1nD19iohQTErEuh5NFomHY61No9WxixGCT5396FOxohRu6NGmoF2fE7zFlCV9WwNQZIpMj9TYkdmlJDj3LKJi63aKY0GbxLgJ/Hax2mCHkQwrIATH0C4RyiBNyWxi2JlmPDpJ2E4lIn/wyz/KjWsHWJfQDTECIbAZcf5l1ZHlBW+88Xv4pf/nL/H0+IRynrO/O8H2G/yQpQym3BFrQd2cs7ebsW46jB5w0bGzd4VNq/DB4l09Tjt+sMf/KC6efzdOeRtQ9t/3+O/7njzPL21mn3z8q3/4N7GP3qR0Dcd1y+Nv/zOmkzsUAg4yg5QvsbRrnv+Rn+Nk7wYXT9/h6YN3MTde4OzkiBt7e8RMc3T8FEhZAyCZz2Ys6zVfe/NrtKsz3vjCD3P16i1MniG3uTXxWUd2+5GKz36J3IpsSR2Uh/ff58H9j7l55Xl0sYNfPqWLOas+0PaCaW5YrS3r2rHeOKppIBeRKs/ItMQomFaGnd0ZpiyJQiGBWX/K5uh91o1icD3rtuLk6RkvXXsVzk4xrGg8hLzgfDmgco0WYRRplUwrTVlqBBneQ6F7OtfhWGGjoLUBGzM2FtqoWFy/hVSGrtLsX7uJbzcpC8JGpDBkJicGh5SpPYlIp6GqqsiN5vj4+DLb5MqVA7SUnD5+zO7eLl2f7JspeTekD7yz6aSEAAaGIVn0hDIkO/Un7hkhCEqx2J/zxudeQdiaenWBIUeVB2T6Cd52dBtPfX7MzsEek3mJVQ7fRDIUizwS4gBEovXUxw+ZZoKLNiVIfOpTn+K7735vLKDi5Sjtk48tTl1KyWazuZxHQ3J5BGtZrze89/77fPrlFxAx49ungbbKiLPIu+9+wJXZAfVF4Ok3fovTf/s1fn0Z2d29wcVJx1vnjzi+eJ+1z3jnWNFl+8R8hlEOLz5gPaRoeFNWaFdweP0Oj+9/zKrdgBF4m/p7RWVQ0mFUxtAMPD56RNN15EYRnUWGiBYSmedYJYllji8Mk+tXkJuHZMHSnhwRmgWDamm7AZnlzHPD46dPkDoRQYVUKTguQtsld42SmqYdcP2KYrHPcH7EunnIcOM5KEpO1j12L8eIwOLaTbrdPbK+w/WWcL6mvH2YtGCCyyC1fOxUIFJbfRh6wtgmz4uCru1p6pY8z7B9R1PXFOWETdOijEFryXw+Z1NvKNUEN/Sk5li4tOmV1YQv/cTvZ3l+xkVdE6Wi9x6VFxRFMcYztKNeIrJZnRNioCwnmCwnxkCmUwEYYqDvOpp6g3cDOssQCPK8GO20nqooEm5/zMS5OH6CbRuapqV3kX6w9DaRYlWe4bse79OmFsaxrFIpBkBriXUOCfRtnwSqISKkpLcWZy1ZDMgQ0EIkwaSAuusROgORMp1k2v2QKpJpRRAwOIuLkElNxCMFiNEOLAj01sPY0YHx98bPug2RgEDKpFVRUrC7v8Nrn/ssWVHi7MCsrKimKWjR2QEh5XiYcayXJ2xWK3b2rqC0wrnAdL5IOheTp7GkFFw/KNBa0buAj3MKUyJki1cBoSRNN9Baia4s1p+TScnq/ILBeVRWYfues7Nlus+lIMsURZlRlUVyPurEPhn67hPj5zE1XgqCG9LzG1pitEACpm1W58npNSnI1p5uSIJYdclLCaNuN11PP/JjZMJvJpG+96zXNRerFXuLKtG3RerORGsxUlIWitdf3Ofx0Rmr1vHS3av8ns+9hlSK4ANK6nEcBW03MJ1H8rxCSMX+wR43r+0Tbc1P/PinuXZtlxBcGs53NauNZb0uWF6sODpaMZ9PKYrI2enAzt4FN29B32fYDjYf/k9UoFy7dg1IXZLr159ZiY6Oji67KteuXWMYBs7Pz7+vi3J0dMSXvvSl/6C/b/3tryA0QCD3FlkPGPWEdduwv7uDO7E0vuU7T9+hGSYoHXj327/KbHXC04/ehfohXTbhN//VLxP6nnLvJgQ4vHLA6fKCxx9/RH96wtl3v8lLn3qVO596lWJxhRgi2aRivrNAqQyEQsZI3dXUqzMUMJ/vkBVzHIFVs+bN3/i3fPPf/td86cv/CUIVBMBGyapOlNSgoLMWoRzGCIJPHhw5QpymlWCxqCirArRAjrTE67vQrwOxs6w+/ohu/dvcuvYS/dOP8INFkhT4rmvouh6VZZftnmqa0XuBrCNKkdqiQ5fEs0WFVJq23pDJHOU10ges8xipiZOMTb/hna//Cq7v0ConypjU3yRWQ9PUEOOoZA+X4w+tNb0d+OY3vnmp4u37PolG4TKLZbvxWxvQYwprjAIlDVrn48go5X+MPmqMUSymijkb4qpFx4GgNWr3FvtK0Z6d0T64j2gviP8/8v6sx7Y0P+/Efu+0xj3FdOYcK7OyBrLE4lCkIKpBSQ1BBtyAbXQTtmBDF7rTjWVI0DWvCEg3uuAX4BdwG+h2uw3RtgZ2S5ZYEotkzZWV45lj3MOa3tEX74rIUjcMkYDbNqGdOImMc3JHxNmx9nr/w/P8nu6Cfjzm1SW88aigrCdMaegnTT8ljBRM1xes6oKbwfL48WM+//wpdrI5++NnCur/YYGdbyR5muJvQ/vmwisBXbfnj/7oj3jy8B6uXPHSOcrNmm+/7PB9jTn/Lk9OC/7ghzfUX3mbH18eGD5XjLVFKck0CspNSyrXiKLIB1+RZ3bjFLFDoI4VX33va3zy4x9zdfGCMO0ROlFoja5LlAZvB4ILFKbi/OIVu8MN62WLlgVGmuzUEVDXbT5ITMEUJWeqQiUIhx31YSSW2QbaNku8h2axZnQJHySLzREpBBbriufPnuekZWW4f7JAAJ3X3FusSC9eUJ09YawXPBuv2cdEozXNmw/onzxBffI55VXP9PQcFjW2kjm9O+SxcT8MtE1DUZbZ4mpyYWuKihgjzWJBomO0E82iZRwn/KxTq0qNn3oOu0ymJQZSCEyDpaqazKYgH2TvfPkDPvjGX+AnH308h6eNbHdX88Aw00y10QghKcoquypmwmiMWSg6jj24ie2+Z3N2j9IYtFZ3wYN1VTPZgRA9KVjGsafreg67LXYYOBxGDvue4BzOeqQx2JTwcBe6GEn4KOfJym0eE7hEDgWcOTkpwc5ahMm0X1JiTBGhVM4w8oEgBIXO72OZoNAKrSXT5HBjTncXSoK1mXmUIs5NeD8SkmcaPWp2u4UYWdUlu2G6W+3ktXSGYxqlePe99zg63mBtRrj7vp9TgCVlWc2W74rgPUYbqrqm7/ZZfyc1i9VqHrBm2nVKERcCpmqoC4e1mf+TuKG3LYt1y3Jds3s1cn6zZ90U3D/e0O07RhcQJjCNAzf9yKGfcDbfp1erBWVn0QrqpqKqAzF6qjoXVjmQNU9VgptQpsC7HC6ojeH68gI7jRTAelnx+ry/a2Yy2n7WqXl/ZxXOUSw+f97ZsiGF4PXFJRfXW955I5/DMTrccMhBpSKS7MDbj1b8p996g10f+PJXv85y2c5C/pKiaokxh2ymGDNVViqW6yNW/YFf/eY7/NpfeMjjJ49JWIKNOWXaObb7nm4XCK6kLlaMYyYEj1OirR9h1MgwvKLbjbPA+U/3+P9ogfLOO+/w4MEDfu/3fo9vfvObAFhr+ef//J/zD//hPwTgl37plzDG8Hu/93v85m/+JgAvXrzgu9/9Lv/oH/2jP9PX08Oe1EzUS0MbJckJxqs93iWiGfDuc4S0jOfPiV6wGxOf/PjfIZYlyo18WjZMumV3eYlIDZNY0O/2rNdHJCG4vHiB6wf2n/0IuXtBFUdenl+zWbbI0jC4njc++AbV0QN8gOef/Ijv//5/zRublrN3v86jX/wr7PfX/Ph7f8jTH/wx49ULvv/t3+fL7/06kgzsCl5gdAUq4ZyjrGpqD+1iiTY5rMxoWDQ1bVNm4eHsSohhQsSOWltce8zm4V/ErN8nBYPA36nRvRvxU9aHeGuRKhc9zru8hw+RqiqJzmbtjNTopPEOvE2opiRiskYmBdJkMX7E7fYsDhNXIc7sBtA6m/dTEkiZNSQaECnvg6UUHB2fcXF1SXfoKI3JsyaZD/dbC+fPurmqqsqjYCXznlQpqrpGK5331/g7Adlh3PGT7/6Y67cKRNJImSiIVEJg9Iogeh4sDM97zfVhh/gkEsaKFFtQZJujULw+3yOKihLNpiw4NyM29Nzc7AANMRdGAv696UiYIVi3NtPbwsXGiCkKkswCWpLjcjfww6cvWb1/xKHbshsqdHHE7z0Dc36KevoKUx3z819/yPj8U0RYE5Y1MtY0hUC3hiQKvMjdUkaRC4QrqaXhwfohhai5vHzNNB4Y+212PBUFOljqymTWRFHR3Qz0/Z59t59DyeYdeiK7HBC0qxXbXY9qG3Ypry3oAunqBnmyYrk5RiCYJkd36Knqls3RCWVZcX11hYwJqTT9MCKEZNVkOpEfPGfrJU+u9/zo6pr+uOCViVy6wJGAZt0gH94nPn1B7R3X59eYdx4i24ZuHKgWa9brNV3XEXzAVAZnc2EupeLQHe6C/oqyREp4ff6KpmnY7XYsl0uG4cBy0TKOA5GEVI6mVRiVr2E180OKouDk7B5nDx/QD9nqnqnOMeuS8taRabKE4Hn29BPaupmZF7lYraqSw2Gf9STeY3ZbDjFgqpLgPHXTMM78jDSnNgcfESKxWi/ZzdcYMTJaz76fSGIiJFhUFarQ+ASDdaQIk/N4b/Ais0cic9ggzPZpORciHi8V2e0q8NYj50NS3ums8mQGIYhT1v4USs/3mYAs5mMzRcaum3keAqEKvHVzwnEuwoZpbkgkTC6LeqXI1Oxv/vIv0iwW7LZXpJDzdBAqr4XsfuY5KLSSIKBqFxAhhFwwfvrxT7l37wGmMFlon8B5iw+giIzTkBH9akSQqBYNJ8dLPn66ZfC5ANsPE93lHusjujR477m63nHTTyRE1gGGjtWqZRoGTo8SwzjSLurccNlpLlIgeJtFsS5j8qVS2Glgd31N2RRcnb+irjRtpbmSgdoUOUV+hlrmvBxJJGtQSNxp4G4bzr4f+MmHH/P1L79BXRhiDHg7MR52WJsRBIUSvPXkFNOsOX38DkJkNoqQhv5wYLvdIaXIBfI4MPYjj954g7IoeHj/FDtek6Jl2HVURUnRLHl1+YxPn/2EfobWeQRGG7bbA01TcHExUh5aEg+52b6A/ylXPIfDgQ8//PDu448//pjvfOc7HB8f8+abb/J3/+7f5bd/+7d5//33ef/99/nt3/5tmqbhb/7NvwnAer3mb//tv83f+3t/j5OTE46Pj/n7f//v8/M///N3rp4/7cMHR2mgqgP768Bu5/GTgKTYih2L+zXCwPq4JllBGwt2Ly/w2x1RCYRwxOmGVStpCoWRBrcsEMnhXSKVgWvncNM1n37+fWSb+Mn3v0ctW6qqoB9e8ce/f0ysFqyOz7je7nAXP0VeL/nop9/B/Yv/hhd7zcWLz9hv9/S7kaJ8jPhAoIqCeAgEBBZJhWSaIkVboIqIMhWJLHBTwlEVhqowiEIiyPRXpQqUXNC5Fcfv/w2Kxddw3Y4URxwelMl02JhQ1YKqrYl2IoQ5yTLmN+o49PimptAadJkzdELCz0K9cZxIIqJJKJGIIpF8QirJydEDPn91Qz/cELynKHOKaKZqFpRlkcfkKWSRGJGb6ysQOfEzBE9VFSwWC4qiuJukOOdYLBaze8DPjheZfxlFWZpM7pXpi1FqTNhxzycffcgfPFry2IycbRriYk1x/20uetj5jhB6LgePTy1tqjhLO9KNJSbFywvHZBuKRjOlhKw0Yz+gC8MwWITQXwiphZhHr+l//GvuWG+R6EopQgiAxMuElqCS4KObc+qXb/Pq1UBdGsR4jXA3ICRer2kbg7YSzJKDbDCmwQmFl4ZlrBBaoYsc/Y7MN7G1OeHL733Aq89e8W+//W2stJSrGlUn/G0CrrOMMeSixieSTPRjz8effsK7b32JRpcZX68M3c0EEfaHHlGWlCdHXJoJNRxYuQr/9CX+/im+qlAqr1vyzzLnzuy2W6oqTzHatuXy8hKjDS/PL2jrBh+yTuMUeD0NXIwDN0rxsne81+Sgt/rRKbbUFJND7EeG82vqo5ZFmzNkhFZ3E7fblbD3nqYpZzcPVE1FdzhQliVFUeC9m9HxG9q2wTrLarVG6OzYUFLcFZrKpNmhlYvkhw8fcnx8zPPdbv65irsCNYRA2zbAnAemdHY4CUnTtPPaMpM+V0XGtmeLa7ZJh90NKQUO+wNVVSHJkR3Ncsk4WULKYX0+RgbnKKsC67PmpNSZVhJm+FkkYWeh6nhbLM8JtQkoRLojZ985DyHze3S+voX1aCNn3Rf4FIk+EZWilIIoIJBfp4wGyLC3Qzcg5FzgCEtKCqXyx9f7wx0A0/mEljJbbLXm577+NU7vHRG8Q0qNNhLnJrour5PbdoFMCqnJq0xTMDmf/9w7qipDK1+9+CxTcbXBOcf1tiP4nnZRUymF84IxjCh9AwhMaSiU5Pww5NVTgNEFvPMImZ2Du94yuKwTkWVJsgEGix096XpPU+V1blWWeO8YhwNKRaJ3ODuSSJljJeD8+adImTjsdxwOI3W7oG0KSu2JEYINWO9xIUcu3MXSM7sob0EvMK/ZA9/+d3/CB+894oMvPcHIgK5b4n7HsN+hy5IQPWXdcnTvEVWVg/9SCvTdnlevL/jo409QUtIu2lmTKTg+PuY3/upf5ejkHtevO8buBmJgc3QPKxL/7vu/T297kpSkmK3zPuQJjNETlzev0Srfr3eHjnJmwvxpHn/mAuXb3/42f+Wv/JW7j2/Fq3/rb/0tfvd3f5d/8A/+AcMw8Hf+zt+5A7X9k3/yT+4YKAD/+B//Y7TW/OZv/uYdqO13f/d3/0wMFIDrwbEaNe2UUfDIgDASd/BARYoBLTxnRyVGGITSRHPMq5cXuJRTMO3oiEIwcsNqtUBRsi5WBCL1/ZI3HxSkYWL0B378g9+nkALrrulsRVIT2hZM48AnH3/IobO8dRQY9EScRkgv+P4f9jS15OYAMgVKPUASVG3LYWuZ3EQwkdIafMj8AK1zKJa3uUBojaauDKrUiBizmEkErE/c9KBOvgnle0xThxAJGyaUlFSmRMmUg6JMQUoOqbIXX6aZESAiRWVAghdkcZ2zOcRrFr2JKEnJEQCboDQGmyIyCaLKQtCzsxOijyzaxZ0OJ0aH0nm3LCF3XTGLt2IMpOQo5yp8HAe6rqeeM0xuKZ1lme2Ch8PhbkqS5mJHa0nygSQzgREE285x3QU+efocUQ3cvBKcHJ+yffEKZQz1suL5eU/VVkSpwXccrUqGkRzaiMEFT6UT4ziHu6nMIrATGFPhvL27kYcQct7Q/P3mDKR8WN7qUIC7j5OAKCEouP/oEV03cP7Dz1FuRVFBChnkpHTFpAqs0Wg30ZiavVogfMPb9QvubRw/2N4nmdM8lZm1GFVZ8fbjt/nkk0/YXl7RTVuC9pRtTd0ssOOEEhpvS7wb0Yo5M0QzectnTz9ntBOlKhiniahBaM3R+ojD1DNYy56IOD1h/9FnHEVF//KcNsDVMNEct/SzrdhucxbM3chaSmKMPHjwgBgS3jmEKYh+pKkM95YlP726YrE8Zj8YPrsZcKc1lYDidE23MKQwUdjI4XJLNd5HV/nwacqCwpgsJpzFsdk+nHVMzlmM1xwdbbDTxMnpMa9evuRw2LM52rBer7DjREqJoetYbY4IIWCK/DP1zqNMkcnAKdHUTdacqGwDjSn+e4JpOafYkiJ9v6Wo2pw9M+XYiPXmmLKss+vDO6ZpnA/2eQrgM5q86/asV2ukUPT7LX0/4pMkhIT3ESPlnFAcqYyZpwT5gAjekYDRea76iaO6RAhyVAXibvIxOZdFvUncsT2GyaFvqa0iF+RBQAxkKOCMX1dK4mH+fgNi9GglZjpszM437+ew0YSUeRVofcgrUpHwKeF8fv+8+/Yb/OW/+hscn92nKCturi4ZfHbACCGpmpxibO3I9dU1ZVnMADrDarmi3+9AKqqqxdnMLSmqmpc315RVhSoVk1U0BowUlO0ZafQc9jf0XY8UefrTu4jbDzPu3WXdlI8cRkdnsxW5Hy3KmOxsFJIXl3uaUqG14igmxr6nbWtEoYnRM/YdVdsSosdPA97l1d0wjozDlP9+hZot1fOaOObXJfl8/xQ/U5Dc4g3uTB4knr14xf/p//xP+Vv/m/8Zbzw8oV4dM+yvcPs9Q7cnSclmU2HKggyQswipub664r/8L/+PfPjRU5TW1FU5TwIdq7bhyRuP0abA25EYHFW9oFyf8gff/Zf86NPvkoSgXS6JricykBBIJbDeIqcdNlNy6Lsd5c+6Xv8Djz9zgfIbv/Eb/yNR4M8+hBD81m/9Fr/1W7/1//b/qaqK3/md3+F3fud3/qxf/t97LFrF5fmITisqlVhpidNgAzRtRbloGacrpm1PpVv20nG4vObn6hIrJc99RLYV/WSpixXeRoZxz6QjYwBwFALWGBgC4zSCNJQCDrbHVAONTFydR16/sHS95VuPW94+8dgJbDIcN4Fvff2Uf/OD10RdotgT3UhVFkglCM5R1UUOodIG5yOLugQSRZEx3Ys624pFYXJHkwRRlSAsqqgp6y8xTp5S5EkDUuKTzPZmb7NOIwacHzN6moCfxnm/ObNwY8iAo5jytENqos+ZLiSZOSvREJMg+swryJdPPnh3+47VYoGeYVApBbp+T9vW1HWNlnmUPE0OO3m6qadYt4gAIAizrTjGRFUVxJg7Ku89jx8/Jsas48gx5DHf2EKkEOJneDaJSkr+2jfe4q99ueXmckJKx/X2Uz57XXHZQVEEHmwGjpsVq4Um6poXu8gwSupCkkxCTQdKX1AGRdFo+qh5vc+uhFu2jJQCH+KdY0fNu3w530TSrYNnttSlOUclChBRcPbGI3bjyOXHz1CNwCzfxJYlPQtEtcJrjQuOflJ89L0bTPC8/XDHcXOOsxN/8lOP2LyDMDnTg8QcpxR58fJzPnvxCUJ4RO2RLuHtiNQFy+WCNCUurg/E5HLSsFZz4q/l9cU5Vzc3tEVDjJlRgZxx5ELw+uaaZRC88cHXmGzk2bNnXO86jq/Oae4f0XUHiqLg/PyC/aHDFCXBB4rj43zQe49SmqqsMUXBaB11U+OjJ4oJe3NOu37MdpA81yMHt2CpJHJVc1CRKuRxdJkk24tLTk+WSCU57PdZh9L3SJkx/0VZYoym7zuMMXMBCV1/oDQaqQTrzYqLi1cYo9Aya5/E/Py2XWZt7M9oAvJBnjg+OWa1XKL0zGCROUjuNqenmrNQpJIImSmlan4t8+GTr6OqqhhHOFxeZFs0ie3ugJq5G5AD26QAFzJl1QtFnN8zISW6yRKZCwapiTMCwIecuxJiYjfkxNtlaWbtgsIJT/SJlMTMi0nUhc4k0xjyZCSBMRKfYJo8CEGlBULkDr4bLD7OwakiIVXI96cYqQpNCD4XjSSUzLuIEDO0IZHmdI282nl4mjv1ew8f5YTdBHWzREmFMXJeI82OLClZH0nsOGKnkRg6Fusj6nYBKeJCpG7zpEwbg9aGo9V9lMkWcKcKimqDKU4oa42737E6qVkuCg5O0sfEYZxycxcjQgb86Ogmm/U8BKTzmMlijM4Bpkqz7Sb0xY7lakFRNSwnS4w+k7hjzCLf+f5pygK/za+pKRTT1AM1xii6ccQFj5YK/zPHbV4Zf/HfKWQK7y3EK/jITz96xn/1f/l9/rO/8Rd5dLZEFiXOXzGOFlMVxOTwds/NxQWmPbA+eYCSEz//9Xe4uLzkxfkNh75HSclmteAv//qv8ODhfV4/uwYpUUVNe/KYHz3/CX/88bexacKYhugdo+0whWFRlVSVwvnbJi0gY7b+y+H/xy6e/289fuEXj5gOCXMuWRuoHpwS1ic8T5pPlOazqqB8/RnXV885LRLnw8DWOfRZQVlUyGuLnAQijtTlF0m9h2GgH0EYcEpgXXapuAihC8hK4fcjZ8uCWgdEinSHjNV2g0UEk/3xSFYlvHMSOTyU7NCo0DMNB8o2h4yFmKFQIk1okUOYTtZrpunAZlWiFRkqpCUU2bWSrcsBJQRm85Ag1uA6QoqoOR7cJkk/DhiXg/WUSOAmEhq8JcyERSH1vEYBoTRSaqQ0OJffRH50s1OiyLoZNBGXCxolcDa/e4qiROuc5rnd3SCFp65blss1dX1Nco6uOzDaCVOULNtyDq4CKQ1lUXPYHxBz5a2rObBLGV6+eJGtfPOF3rQFk4so05L8gExfeHmeHK/51rslw81Pudl3OCIPz464vDR8ehjYNJGWgmfPHOViQGtNUxrWK8M07NnuB1ArnJ04qjWicbzaFfgwEaTP3eOcZ/CFkfp2o59vwNnOKu+KkxgzKRaRp1T333jIzdUNh/NdjlyP16iwwIUF6ETpIJgSkWCKih88HVgWiXtCIDdvc9PtUcc1k5KIFFHMWUtS4Jzj5flTROGJwVKarGWYpimv89SA9DKnr8qEiwqFJIVASJHr62suLq54ePqQUqrMrIhwPWxZblY0TcuyrNj2E4+/9Yuc/6CgLQ1bHWiN4rDds9kcs15vECJj/lWRD/EMrSsJwbNcLxnHke3FBSdNmS3CSnC/Lvn+d/4I89VvcbHt2TvPQ11D01CeHWGfvkRGTZVg1w3sd5mbE0OkqEqEyoycss6psXK2DafbdVvKeqvhsMcYxX63zQWSt3gUh2GiqmustSg1UM+hb1LNVlgh52DQfB1IJdFGI12Od7DWMcy8FSEkUhV453j96iXL9YaiKJFS0XcHxnHImpkix1vEBHayHJ2cUlZNLoYghx9OA0J7kBNFu0TM+VrW5QmHRNDbfHjeFgwuBELIadZJwm7yLOuSFLL7wvuZqSEyAE7NjjTr8p9HmTBFkfkms3W2UIraCGzIq6HbROKQ8op1HCfKQmOkQJGYfLgr5p3Pz9FzKrq/BeUBm9WSv/SX/zJvvPtOZqWMI0O/Z7HcZAenmNPUE1lPpBVFUdH3I26yTNOA0oayWaCEJopACo79fpvpziGSfEmUkkV9RN0cUZgKGLIDph8Y7ZSLyGgZB4u9O/wh+khvHT7mVZZIiTikeWI6Z+X4yGgDg/Uc+om2H9je3FAUmrbVSCmwY4f1nqpqiClre0TM8QBCRrx1pJgFzHHegfk5UuE2X+j21y2pVpATtMXtPcYH/vCPf0RhFP/L//lfomxXFIsDNm5JMsPkpn7PT3/4MYNo+MVfEcToOFlK/sqvvc+PP37Nh5+85Hiz5C/96s/zla+9xdDvGKcBTMHi6ISDPrBLz2mPNEdFS0oaLQ1CljTNEcoIimi4nq4wek1TrXl19ZpSVfx7zsv/wOPPdYEyXY2UVrASmkIr7j94zB+tSj6ygTFCKR3NvSOWbcIPW0xrWNiAPVE453nVHdgNESOh1IHH9xquryMPywXv+Mjr64HL3rMbAhf7iQGJPUxsR4OMEe8L+j4QY2KYPEetZLFoIAm8jZSlZr2QdP3AW/dKXg5z+BYWHwy6aCjFyDR02fIZs8UOmXkgen6Tl00FZQ68yiEWGf2OUDizxE8KHTNUyRQFzucbB6FHhLwS8s7TmICbLEYIojtk6mxSjP0OiUBXDUnlqPPo3Rw2lfNOfApZfxItKUw530F5+smRSGilKIoCYwzTNHHYXpJkAUFw+foKQg5Nk0qyOTtie/GaVdsy9iN1WWREf9KUbYl1eYWybipWdZlXVUpw/+EG73tOTzYMI3z4g5HhkGmUgtyt3jupabTlMPQEJekHhUsFu/NXPGlqCiMYh5peGg7TQDU5jNRc+YGEYUhQNUeIMBd8LrGuPetWcDmmu5UNcLdyyo8vlOl+VtxLqbLA1+Ugu6QVDx4/Ynt5jd8PrMsGISU3qmM62fH6zSsWu2t0X1IdCjZqgag1un2I9SXV0Yp6uWR47RlEgVcRFfNEIk9wMvRtv79GNYK6qNlf72lVtscnn+i7ERlyJ2pqjS4NNmR2gvcBUUDfT1gHwmR8uYywXC/Z7/e4YeTVbs8bDx9waT3i/S/jveXek/tc31xR1CsOfU9V1jh3M5Mws1bgVo/TNA3jNLDb73JEgXN54mE97dDxsGz46atX6DHyaj/yTrNAVorq3hGNkDgf6V+/ZvP4y1zfbHn4+AkpjXfriUPXoZRks9lkfLsqCckRU15FyDncLfjA8VFe5RSmoJ881jnqtqUsC6wdsdPA5uiYarHKIaMAMqcVIyESiChiCmg5a1dUtlenlNhebylKQ9uuULqY2Y8CKQVD1zGOfU42L6oshJQaJTXeWkRZYrQizY3B5vg+R6cPuKkWvPrkkyy+TJEkssg1AknkCYXzc0DcPE4X5FXOaN0MQvuC6XQrXk8pMdi8oonZo5yLgZT/zkLOYXxCzc+8hS/m10XNdmjvPUkIRMrfk5pXey7mVW+MCTcTfJMQrNqab/3ar/DBN76RXS7e03cHTFHiQ2R7c02MEa01ZVUhpcSONk9z6oq6qXMBlW41LVkHo3VBXRtimjGbcnkHfYzJ0g89PowUsqcoNUFIAjmUz1rHNKOWtMwJy86HrKKLeeKcNXwZHrdcNAzDyOQcgzXsupG26TFaUteaulrnld1hx2TtjJ/PxgElwKVEXZc4n51HclZbO+/vmiApBHGe0Eop0UoT8LMAXMxuofxcax1/9Cc/oTSKX/i5JxwtW6oU6buOQ3cgAUdnC26ebnn58hVtnUWyRka+/NYJJ8uKzWbJyaZGiMTN5QsEifbslFHsOLhzVOFYrhY0VYUUNbv9NZWoZ8fmiFYFZ0dHjBNMdkdtSvp+oP4znPF/rguUfjvylXfeR5UrttMeU2jeqyZW/jVGThgiqlGEwuKWghqDF4IuOrxRvPnVNo/wkqDUCmcikxeksufRuubsyZpXFxOfnw9M5YR0KaORXY7a1rVCiEjTwL3jxEJatO9ZlwvCJBl9oCoFLgq2O8sUSlzaY4crJnFCpWqwMo//Y+7AlQIxp1tKIsQRKWbW/HyxJpUQyRCjYNeNJOFJIaEKSfATtnd4JN5ZohJURhHchPUTRog5PMvnz4FGFRUKSXAWmXJCcEyB4CI5uFDm7sLG7AKS2drX9Q5V5E4/WEdRFDRNQ60lMk70k6WtchBWWyjWzZqyKFlvBOWTlvWiRcdIVRq2tod0SlsqmqJk3R7lwqfQIAKmyDeCaRpBaPoh8vF3P8KTb3oyL2HZDRNXu5I37j2mev2cjTZod8OjdaRuLalp2O0Ti7ZnVRu0KtlPI593cD3CMAmmi+es64JVs6AcBCd1ZKET1z9jfY6zGBK+EMnG+aYFIIT6onhRAq/hzXff5PLVa3Bg0DhnmaJlePeMFz93xLAAe2opheDm41ccXm5pO0DU2LGksWt++skBX9xHL7+MDNWdSC7GSJT5QGqblqI2DP3ANNgMhypKlpsjooz0u45SF5jSEHyiqAuSihhhOHR7Xp4/pxu+TFOtiEExTANlUVLWNQ8fPc4WXFOQKsHqtMT1fQ6qk1BrRWUKSJJ22WbXlUisFgvcXIhst1vaxYKizFj0+/fvM44jj994wOv9nkfLFT+6nOh6w9NXHfH+KUZCsWgYBkspIe08jZJc+4wcz7bTgsN+DykRQ8RZh1LQ7XdzweEZho71cknf5yDDuq7zBNF5lMhC0Mvzc46Oju7WA34+8JAyj/YRM8PEzoLjHFLXLivK0jAMgJjZG48fQAjospwt+IIYAuPYZ5ozYMeJ7tCxOTqmLKusKEg5l+Ww7+9E1j54ZJSUbctys6Z4+Wr+u+ZpSZ6KqPl6SPMqJd9XUkqECJ9f7nnzbH2XwJzD6+Is+M7ONB9n3YnIhWyatVXee8qqQCmDIGR2SswWZi3lF+PE+avaEGZgnUSKRKFFztUSEqRAKWiamg++8hXe/+pXmaYhr0PIRX6I5OA6U1CUBVobxnEk+Iy816Ykiay7kkhcsJkqLCRS57WO8w5nbdYI4bAThDji/Atu9q9YtQuWTU2UgtVpQ/X8QJybjZCyuDpj4XPhEmYsAiRikgzjNLsLbx17kt1h4HrXUZeGujbUjc52ae+YbOYAjeNAVVeUdUkMbibMZn1diFnM/gX0Ucy6vPiFU5D8MopMysOHMBeJMq+ZSUyT51//2x/y+bNXfOmtEx7da7DjwHpVMrg9x0cblsuBf/J7/xIfBKu24sG6YBwcbnKkYeTi6sDJyxs++MpbFEeKUHZ04RrPRFUrhE74MOCDp2pqClOTvCAEx+gsk1MEb6nbCiU9i7ohXfzptaZ/rguU49MnOFNB2dC0kt30GnsYOcUhAzgP0Y8UyVCnDNPZup6tGBmUQys4VgVV1CgM3RRoyxKXLK9uLDjPoqj5uTcbHj+uiIXPacBJIkOiyjhVHIJf/ZUNRRS0OlLqwNmyYXQJ3VRsloKj0yVXN5bOdjTckLjH4CTGaQqtmaaJstZEQEuQhULLiBJTdu14D9MEhc5vhJD1Kh5B8BNl0kQ3YLs9doqMSJrlaX6uhBQcLo65mzFyrsJN9uRHP4tyRwyKGAVJaaLInY4SGS5WKJj6HbotgIKmbkHpbCMkcXx8lC1ohwFvc8zA6bHmg3dWHC00X3r0mCdHR2xWgYaJMA7Y/Y6qUMTimDRs6a+vSUys2hYvJRZPEAUiSSoanu16bsYrUDXB9aRU5u5OZPjVs71l8J7Fw0RtAjFOfHI98ObxPZLbUxnHy2ggtGwPEeUmjleSJxvJ/W7EPDzl407y6vWel59sWTcblK7YJcvg9rnzvC1E0heQtvQzIkkAT0RMFi8Eclnx5htv8fKzZ4jRUZoaNz8nlZqzr32dn6wCKiSmWrF89IDdWcv+vGTbb3lvfUR1Y9j9YcSMG0J9RDAFKIuKOqsXhcoWYwSH7Z7Wtxz2B7TIBec4Wtz1NYXKRNVpcozR0qiC5WZFXZT0h4mx73n24lOut+ecHK+ye6RdUFQ1isR4yJh2vVzx5pM3+PRHP8FPY+4UVw2jnWgXS56/eMlqtWG/3VMYfceAEUJQVRWTnVgsF3cC6KOjI3b7npQGgk3cP/mAF5cdL68lY5IYHMVyzbXzKHtAUCCHjuOzB+x3NyQfODGnlEWmr3rncgqwkCiZD9u6KlEiEWaR8zTlkb61lpQGEIZl02asuHVURUmKkf3hgDQluhQIJELB9mbP06fPcnGQItY6hIK6LmkX2cEjpEBpgyoKirLKHe8M8WvaBWmGcVnnWRRF1qtkbzFVXeeVADkYLobIftbZlGXB8uSMqvocKfe5EUkzgdZnouwX65eYCw6VX4PtOLEbLPfWJdNk7/RUGRqSKPWcezVPvRIJG6CQOcNdqRxMOs1i4ThPrUKK+MkhRNZZKSnQs7hWyTSjB3KYqQ8REaBpGn7hl36Fb37rV1muFqSYUDOTPxdbOTjRGINzlr47YJ2lLEpCsnS7HUVRIGKkrFsgu9O0KdFC4N2YOVKmzO9bt8VOkSgkU+gwusAUmsSGe/dP6a41H/34GubrxfmZeivELMJP5LiPGS8wM2v2h57JWjarBSEGbAicX+8pjeTe2RJnJ/YHi1FZU1coOTsab52MgQzCzoWmD2ku7CXGZCbJ6Ke8dosprxrzFYZgXn2F/PMn5amUzp0uMcHV1YH97sBP2pJhGFmvKjbLijefHNisG95994QXL/dEBJ9eDVk7hMJ7xaKUBDvg0g1OWEISBAL9tMd5mI1dONeh5DHXN5fEGNC6pO8GFvUSGzKJ2QXQcrrTdP1pHn+uCxQpyuxgCNek8gpXHlASZNIEr5Au++b3OEieJilqVXKwLmdSCHAkCpEoZKIQkY2AKRV4LzMSebdjDLBpNSstqAowJKSBQhmcUwQCWnmMTORwQMGqLtBS8eaDBi9znx9CxeQ8k9nxOjmmPpJ0mXNkYk69XCxqpIyo4CiLhrYuUIVGGpUZFoic2Jk8YRSESRBCj5dyzqRxmTniI9YNKBFzXJaIFKUheU9IkJKcw7cyNVWogpA80Q1o04IuUGpW9qORqoI4IqRHi+yxF1IgpGSzWvJ/+Nv/a5abmu7pH3N9/oKUJmT0fO1M843Tr9PqPIUQ4xX7z5/iQyDagB09tqwZ9ze5GDMNzeYB0/aafb+nWlQs1qekScDQ82BlaBclNhi+9fVTbvaB7XZiv++oKkOpPe8+WbASW64vF3QTnHeCk1byqjM0vuH7n1/yZH2EaSODjwhb0fiEEwXPLwZ2Q0lbCDaP17w+THz46QU3HtIoENrcpZOmnylIbouTL8iyES8Fy6MFqwf3uHx+TuFz9kpIGShnVEX18JTdegHhiqmAhVRUSrG+d59PfMSenXBvdULxBtjxnPECYhlZFBO+K5mEJKkA0mV+RUgEF+lu+pzhpKFZVCQlmXygqEtcN5FcxBhDUZYc9gOpFlzf7ObVRaSfOkKMLJdrZAz040hblhkDLyXH9x9kt85qwYP77xKD4/ziNTe7LcenZxRVhdKK88sL3nvnHZxz7Pd7hBCc3bvHT3/6U4ZmoKwqbm5u8jRKFVSVwZ1fUzUC6yQ/fd7x7Mrx5iJS1kusMUw3LwhOs/38M4rHJxSmZNcdsMOIMgaZwCjN1A8MsaOuSvaHPVLUSAFFWbLZbPIqcrYdO+cpyxqtNd2hY+wHJIKj0xPcOBJjZOgHVkcNKcHl5RV9n4XmwTmcCxnrv+05HPY5tiGCYA5785EUM6ArO770HCaX+UcxRKydqKuSEHMnXBRZWCuEBJFpvClm0eb65Ayt9XxkCnxMSHF7sOWU73wE5utSSUFIAq3g5c2WVVNSGUU/eczMQUGAEhKpJYmI8zEnpc+MopgiZWFwIWB9YHLzQZkywDEXgrMT6O69kdcjt/oRBBRaUhUFX/7y+7z/wQcUZbakl2XWDWljcoBgzDZba9MdQ0YpQ4iCqlpQmEBZlZASbg4qFUJycX7OyckpRVURRps5KEKg6wIRJoRqWKoFSvRgegrdsN+/xoYtl4ctnjivheY8s8icOZgLF3nH8RDEFG6HVOy7gThrcibncjBisIyjQ6qc/4QIJHKuTj9mp1V0FudcZvTMKAatFEYFfMyAuZSyc+cWdX8n3hbZNCGEnDVLsy5FyFycz7+fYuT8Yp+ze7a5Uf3osy2bTc3bb53xjV94lydPHhG9g+gzK6va0HeOi5fPoIpMEezk2F5fMY59lgI4yb7v0EbhXI+UCaXa7H5CMtmcsxaThzjladv/lC6e/396FG4k9g5zokFnC62KAoKkEBohFIWUKGfz/kwYiBolFyQZsDg64RhwjMkSZUZ7l0JSlQnZRMq+4Nml5eWrkf2lYbGQtAvPohFEHQhRYlJEi8xIuK38IZJkrmInn3kQSEWSHtjnalcqlK7QoaJSEplGVsuWZVth9zesVwuWRwsoCkSRO1+UgTnvRpWCWgX6KdNSIzmiPFNtB9xwQJYlY3BoEh5PqdXsKMlpmELmIi577DVJFERRITCZX+IhBEv0A9vuiqZpUaYgJYWUc3cUHXX3gsJVbO2Ac5lGW6iSVWNYtpo4XROGAWygLhf0r1+gpGS12QACaQ1XF1tkW8NouXr5Gf1wYLNeYTc3OBuo6wWpLKjqFa0K/KV3VxATIUVudgP9dMAozaIx9K8tz8YdNtV89UHFsxdb/uVLh1QjRlb4bc/Z5DluK17vIt1oaZctVep4bzWxdwXP9hNlu4Ybiw+OotEMoyORQXKScLfeub0x3+7jSxTq/pr24RnXz19RJJV/hlJQq5z86vY97777Lt+ed9EiSGSpaYVmkQxCSk6aBVXRoCvBW//pGWGUvH75gpvzD7HnFeKmQKg1Xi4pZUHyOaOmaVuMLrFpxCwMqizQPnDY7ZnGkbqoCMky9ImyKjlsB5QwmFJzs7/mw49/zNnpfcqiRpPQugApuby65rDdYp1ntVlz7/4ZLuTXZ3vY5d39bI8PMbJYLO5eG2stR0dH1HVNCDmJeJxGxnGkKEvWR2uaxQIprtgsC+oWfvDJKz568ZjmXmIdoD07pd49RXnP9fUN3fk51dk9hr4neM92u+Pk5ISyLHn16iUvX77kg69+Ba0kwbrcpf4MSO+WuxNjxKgSoRPLxYK+65jGkcN+T9E0hJAJs85lW+ann37KdrujkFkgHSMEl7ApMI05ITzFSCLSD5a+P2Q8mhTsd3vquqFpmrmwjaQkMGVFEAlTVlRVmVdXyuTUY6lxduTy4jq/XkphmuoOHZ8PhFmQLWZ4YIIQwyyqv2WNCFyIvLje8dbZMVWZp0Q+xDn/M80TFZnJtvM6ywWHdTkQkblwiDFPE7IWJqPXxazZkCon+grxhZg8hsSizJOiJ2884b0vv8PkxmwB15pEou+7O/S6d26eqORAQD1jKJTWlFVGycd5DUKKTHZgHEaklNxcX9IuliitGUdLDAEXtiwXiph2eFchVMLakdE+RamKx+8s+ev/i6/znX/1lG//qxccptsVboAkkSof+gHxhXU+CmIMTNZSqyq/PiI7nrp+oBsykK/wkSgzT8e6IeMLXC4sMiY/EuJE3Ri0SnkLpiQp5OJKKplTju/EsnEWyd5OsWSep8isb0oxINRtHlguQP1Mt85u0cDl9Z59P3J+1XO9HRhd4PRoMU/PJC9/8oqxjzx52FI1hkl2qOTYrBr2yrDbH1gsNoQEwzTR1g3DeGC0I40WWfhegNYWpRJCJQwzzuJP+fhzXaA0VaRdKFQ5IAsAiUoaIQ0KhSOA9BQyMnlHpxPejyQJjVRIBAHFGByiiFQIlspQyLx/VEmyrgTNScXHfuDlpeNyL6mrxJP7kqO1QOu8Y/cpQpR3aZMyRayUWbE+2z9TTFSqRIprinjgEBXCGETSFAUwTdR1zXrZgNlQaEW1WoFWJKURUoHMXRPGIIvA6Spx0TmsjZRVdsYk7yiEZ3IjXit8jNQq4ZMH71ApEIUhqRIti7wGIgecJaXxKotzSSCFIoqICBOmajD1EqkVipKYXN6fEnGMbO1IUIKqbkguslosWPoD+89/mJlxqmS5PGX76jVDajnebLBuxLuBoZ+QqmHZLNjtrinrJd4FClWSpimD2XTksL1iYQOByOizwFhEQdOu8a6jUhUmFahUkNDU65aj1lKW96iPDlwfHIviiE8PW1aLNU4qXm1veHy6RHiHC4HeJ9bNEUo7fnx+yThadL2mahR17fjKu+/x4Ycf8er65o598rMPoRSb+2c06yWXn7+kUYa2qNn7MU/YEHTDQKkVp2dnjO4CREQmidAKoxQmCpyCQmsaVaHJ+he11jxevsXZOyM2DPz05We8fPYxxaUknXto88FysHvaRYuKkjEOlBGqsiLUJVpKpE4YrdFS4YaRuqix3jKEiUNd8eL6JWOYsMGzXB3R73dYG1gfHROCBwX7/ZaXr56zXq8JIXB29hBvPW7yGJkBd289fsLV9RUhBLTWWSwrJSfrY0KKDN1AVdccHR0jZGQ39tSrBpe2fPmDD/hXf7DjD5+94JtvvE2zdHxv6vjKlKhTYOwddbViKkuqpsK6KY/mnUVrxdnZGTEEhr7L4WrcgtccSmVNRUrZTXd+fj7bWRXDOHF0fIz3nnEYKOua4CxmscI6x+Q6/vW/+heMroeiQCuJD4kq5ByY6ATSZMZIt99zdfka58PsYMornsnmrrlpGoKPRAXR2TxtUR5rR5y1LJYrxnGYBbSK3b4jpUi5rGiXCwqjOIx2LojSnL+TC5QQ80Q3psjkLDFle7Axmt56lg/f4MHxkg+//wN8iGgJt9ERcdaP5MiN/LGLeVLi5olRuCO75UPTzeJfJTVSyCzMJhdMAoWMHlMqHj58zFf/wi9w79FjqrZBAF13oOs6hMixF3mKIillQyLmQMWixFlLVSuIuXDwzuXDXgi6/YGUJHWd042zBTxfC0jop5Fl087TpAkfSqIrGP2Ops72648/fMVPf3SBtXNmkBAUurgLf0Rm3lL8GfHq7VRsHC0pRZSWTNbhg+bqZsdysSEJiyokphGUUrA2C0JwBAd+8oioISV0qSgKj7J5+qFn3Zubm6Fb7dstpC3OpGEgGzBSQJO5TXH+bYG4E4gzuwtDyFofFyK+G/mTHzzjxas9Wue1UtMu6A8jD4+P+KVvPKE9MiQsPli8S5SVQ+hASj2np0ukOGGaIqbIoYDWj1ms60ash02p5mvD48V/JBMUYxSiDoxiQCeJnqv9mCamlKt7ZUCV4BGMKYt/tNdEFAQog0RKg40Wo8CI/MaUMf9gjUlsVOLJmcbGyMUuse8Tz19HvFccrSJNmbuGRJ7AZDujZQyGqBQyTBgpkGmGl4lrtHoO6gkRSYh56lEaTWEkbaPRzYaqVOiyJRZinprMgjeRL0AtIi0TIlbYkBhHT1MXs+AKwtSTpM4XOQknATEr+6VG6iwATH7EjyPOO4qlQZSR5C2IEm0UhEjSikKbXJ2jcjBWcDhvCSlyETuSiIjB8dgccbasUf0z7Plrxssdm5Njxnjg9flr3DRQmCXXl68oNZgkSMlRbY5I2lDVFV3fYceRqSjwsqIsDIera6Kd2A57pmlkuVhgh4lCt7ibK6ahozw5Yup6hq3n+bnl6ChibWC1Trx1bFj6A+0mWzFPysRPtgfaJpGGgbceVtxcSha1QaRrNqXgl95QxGrDH37esz57xF/7tfcoXzzl0a//Ot/5wY/56KMPISacyOmjxmiO33iARvH6k+cUJnd71ju01AxDTz91ICJvvfcOy7Zhv81dlxCCSmZNUhGgUYJa5J9ZHRRCSoogAcMYJCFJHp28z717oGLk2eefwQ/OKVYVPkxYepBQpAIZBGVrWL2xYnfYUy8khdbsrjsOXYdJ1ewgSBAT3X7P88+f8uj4CdPkQEqOjtYokbNNgrdMduLevXtorbHWZieOKfOEjuwA6fseIQQnJydst1sKY9htd5wcH+cOT0mOjo/RWjIME9vrG6JL7PbnDNUD7n/pAd/76Q/h199FFwVBwPmr12xqQxFBukC32wOJvuty/sfYc3p6irWOo+MNl5cXnJ+f8/DhQ5omB6l57xiGgXEc727wXd9jyopxmggpUlYVZV1nncMsuEQo/OTor3boJHIHTZ7eh5loHEWaheiRw2F/h4pPZLGjKSqszYLOoizymkJILi8uiNERfcw2aaGyQDcFpFTYKWPSm2ZJXddsjo5pS0M3WpIDn8JsS87Y/dvu2keP83mF6oizYAAAvh9JREFUo1U+KOq64ee/+Yu8+cYD6qrmO9/+NtM0klImw2YmSoENIa+MQiR6n1cRSiDI66E8vbg9cObAOZ0F4pNzWO9ZNxVVoWiaiuXmiDff/RL1YsE4WQ5DN4MdBUpnF2DVZDrwNI1gHXXTopUipjztExImO96tsrwPaGNoluucAxQSTVvhg6co2zssQIoVF/uJ06M3kWYHgKgjtVqw7yzBS77yC1+i6xzpO+eMnwVCVDm7SuYDP4p5szKvTSC7a0LMdmApRGbLkBgGx6fPd0Qijx5U/IVffYvN/ZJmYWiWK5wdGA4jw27ADsNcVDV8fv6M7iNHkDK7c1xeb+VJX57Oi3lKluLMQjEKKfIUTNw1TZmVM9m8KsqT3jR/33kKFL0HckbS5dU+J1hrRVEMVKbgjVNF2xq8PCCFpxRNfi3UgX0vcB6USrRVQsiI1IZDF1BBYm3HupbYOVeuLDQhuj+Ly/jPd4FC69nJHm3m9NsYMVKDSASRleBFKCmpIHlCGElCYYtIzwEFJC/RSNqipJAJMYNwlMmTB5+ykOlkrZFak+TA9V6wnxLdq4nzG3h0VLBpBUUR8+RBGbwqOThDGQR1dBmNPHcwCYeo90iZmFzMabtG06hcYFSVoTSGQkOSCqkVKbhZ9Z+/vwxVi6g0UEjPVa/w/UTwFSb6bGv1jukQiEWLLAp89KhSoXWDliV2PDB0N5RGz2PqACEQxp5CFwgKIKBVymZKkRBEop+I3mOIpOhIKWD7gYeLYx7fe0gxOqYXV7hpT78f8KYgqoJpt6PvdizWDUI7ltUxY3fg6Yufcu/eEzan9+i3N4y7S7rdDW2hSWFEJU3sO8I4sVws2Hc9y/aE5AOV0UgN21ev8QicW4CzuOmatx5XPHpQ8fyzHc8+uWAbNWtdsPbXvLtRLOqJ40VJZTbcjAcGJ3l4uqYuR/ZDiRdLpD0QbC7E2mrNf/Xf/gHH1cTz7zzlV3/lmzw6Pea//8M/RsdIMJF7777N/voGNUUezAGZtxZbQqCpG6y01IXinfffxUkYUoY1RQEqJtIszFuWBYZEkgKZKXCQFD4FkswHmdRQakUTDf3REbq45uj+EV23pSwUTVVjt4HdYc823eC7S4q24N7pKcF7psuRctEw9h5vA1qrOQBv4OrqgqHvODk+xZiaJAW73QEpYLITbdtSFAXPnj2jKAqOj0/oup4UAnWVE3kPhz3L1YqUEsvlkhAC1k+8evGcR48fo7XEupEkAs56kvWIyWMqy+j3iLrCLJZ0zhG04uzxQy5urqjjAj1N7F6/Rr/9iNHbnLiLYBh6pinn/YxjT1FolBIMQ4dzluVywTAMXF9fzzlPuZs0ZcFkJ5TW7A8HNpsjSJnYWRcFISaEFlxfXbHf7iCSgzhDXncEYi7SNZRtFm0noG4XaGVyou1sh23aNid8lznhOMWEvKfyikNIfPA4a3P2jHcUBbRtPTuABqqy4PjshPWi5aYbsN7nyUfMrJHeOrTKuTpSKvwtx8Rkl0dT19y//4DNyX3+4l/765ii5OMPP2SYJq6uLvExO0vKwjCMlkAuwC4OPe8/OCF4TzdZUhTzepjZvizwMSJiFudWRUFTNxyfndIulyw3JzTrDZc3e5o2T5XatqUf+vnem6dcXZcLlzCDvvytJk4YrJtQUpOI8+vjsTZPSjIjxdD3I9M00NY1sihn8Sh004A+fI7RCSULOuuojSBGA0qyOi14/KUln314hZoDQW9tvUqIGemfBaxp9jXnqI2c1RTnAl+KTM3e7izTeMNy9ZDH731ArEeaeqKu7lPGKxZHRwydpx+fE6XiuH6LJ28eeP7pJWPMU7AQM1VYkAdWeTKVUDOcbaYvffHvWfMjYHZv3Tq15nNTSIRMmUouciGNUHcTFhES1noKVaAElFWbNwQImuqURJchjvqU/ZyKrZBsGsHh4DGLmhRXjONIXQak0QQBioSkYPozVCh/rgsUV+6oy4hMhhRFnl7EXFlqKQhGMfmMeu8nD1JQFnn5E2dUexCRkAJaZdx7mJHppESU4JyfKaqRqpScHoEPkWFS+GDYdtAPgeUi0taSyghK5dDKo2SkKrOAresD3gsSBucn/GKLbCXWBoIpQTmUklRGYbSmKhWCQPABleS8Y4x3CnIArQu0sSguSdwneIsbM0RIFwojPHbYQtXi3Uh0PWNIWCGRUWDtAYJFVi1luyARmcYe4UZ0u8oCXgEp+Ky3CA5kFgUm5+nsgGkWFLLkg5MzTpbHuL5HKMFis6C7GFHNivtHa65fvcCNE0SJHxOyCaiy5ahZUZUBo1a8ePqc4fI107DFOke1XOOERY59fsMUJdvtARciRgcKpen3W6Zxx34/YoF2o2irE8RKcRjO6ZLkJi54NSm+f3HFu8c19xeBclnz7NAx+R7XTzSrB3x2uMLvd3zjSQKveXXQqOSo2zUn92p++pPv8c5bH+Bqy/3LG7rv/T955yvv842vvsUPf/IT7r//Hi9eXxJvBs6OjimKIhfOw5DFhlpnLZJIPHpwj/sPHvJ8xnxHA1IJjFTo/GrjYra0AjMW/Gd30PlQUNJk5oSQKJN1Ss2ixk49h21Hvx3xnSOISN3UVFVBs2jY9z1t07JaH3HT7xlHS9+N1KscgHa5G/ln//3v8e7b73N67wFGCl68vEKmiJsG9ttrPvjyl7m+vmaz2WCKAmMKnNtRaJN5OMOI1hk/P1mLnVeYpihwzrLbbynKAiFgHAf23YQICTMF/PWe6lgy9ZaqWvL6ck/JQHOyoT9ecf3iFfXFJYV1tFXJ5f5yLjTydOTy8oLVapmdMM5i3cTV9cTx8QlX15ezRfgWMljgnMN6R7tY4lygHwfUwVDX1d04vCgKfII//OPvsPMdstEE6XF2IuFJKuG8wwvP+mSFVIq6WbA5OqKu6lmHM81I+Ug/7JmGwHZ7Q7tYZbijUngXuL7Z0tZ11oSorKcTQiCVYep32dGzvUGIyLKpiEimw0BIEesCibwORiSUhKbOIDGtMuBNKYUuimx9Noav/eK3+NLXfx6lDS+ffs7F+TnjONLtD9xc37AfR07PTum6jncenvH9P/ljUkpolZ0kNgSsyzNeHQOTC7z//ld4cHZEYQybszOSlDSLJcaU1CuFtRmKVzcLjk7uoXVeCzln82oq5u/XB587e5WnM0pDWRQ4azFmJrQGl5PSi1s8QqJpF5RVRVFWs1B5QghPdAcGr5CMaN3gPewPOzbrM0TI5ovoM79FyLziyvqXTN3N8uP8EHMBI2SeZoj5925jW2LIwt37j08w5Yar/jkvr15yui4Z7HNOljUhRQ4ejpcLkuhZHiuEzNNMN8eOZIFtLkVuBchJZk1TnAuovEpLSAFKcBe5kTE2CqXyilXM9xIJOaE7zZ8vb43IQa8aLQXH6xJdR4r2CJMEMXWQdmgN63XNYnHKNHr63nG9f0bSltbUaFnSmwklFUXZMDFSmYo0FezTfyQFipdZOa7SbWR3QApJclkIh4QoAqMK+Cpm37iKFFISo8C5mbwqc6U+pjwiY+aQGCFISuARWJdHpZvGEFeBq62gGxJOJjov2G8TZh9RKtBWGeS2MJbHxwqtBL2VbLvEfhpxKVDGidO1IgVLUolxGJBrnbUIIgfxSSNJYQRRklQW/woxH1kxB841laGUHYVSWBTC5ErflDWlT3TdDdvtaxZ1hQg5YbWQgkIptEw0qzOCdzM50xB8FmzWek5MSCAIiOTxk0dhkDHgfZety86gRcUDVRD3HVopnB0Z3EjQksKsET6hZp++7SfW7Zq2PUKqyHDYkibNFHZsFguMsxyIRDkRTEl7tKZZnfD6+Suud1tEtAQR8K4n+IA9HDBC0fUJ0zQIIl1/Sd8fKKTku3/8KTehIsqWr54pzprAfgwM2tLIgcYsuFyUCH/Bz20EsZGElPg3zzyrJvJoWRMvAxdPL1mdnWDEQG1b6rff563Lj/jx977HR80ZT957m6cff4pxmmqxoaqq2QKoaJomu0CGgWmaOLt3wi//8jdJWrJ3ligFyBmPLxQSCDIfiEbInEotcxpuErforfwQczKtQOauMkWCdQSbGPceowxlXYBOSCVIIbG/OdDQcLi5wu48KigqXaCXhvKoYHlqWCwa3F7wvZ98l3ff+YC2rjj0PZtFC1Kgtebm5obNZpOx5lXFbr9n0bY0VZUDAZWm73oQ2VJaFAXDMHB6ekpZFXTdAcSCyU0Z+x0cy3bBvjsQrka6ZgtyTe96IKGrilQqvJEo77k5P+eedbhhhBTZ7bYYU2Cdw7mJosyrp7OzU5q2ZugHXr9+xfX1NSTJarW664DbRcvkPZNz+dAWkmEaKctcvIiZ6jmNlu/9+Pvcf+8xfRy4fnVOTBaSy9wMkXDRUbUFxmRK8/XVFf08gSmKAqka7DgyDBnBX9UtADF4jMr6jeVqzXqxYrITznuapqKes3+qsiamgBsKCqVoCoPz0GnL5FyOuMhJmiRCztSSgqrQmXCcciTA08+fcrRZkhLoqqZaLHDO88E3vsmXo2e/vWGaRrpuoKpb6qZCSYUfOz7+8McsR4sNeSKkksQngY+CyXsGG3nw5E2+/MGXiElkIfbhkJuwZjFHEGiW62OqqqYsDP0wIQjUzRIfLMIndFHAvN4QSlOUBVJltoZUBqkFznrKmdCrTSSYvHoaxxHnBUWVxbtGawSK6EqUEVSVpCwfYO0BQ4dCIs3E0YnmnQ/WXL52DHaebqaUnUizsCORIwiyWPa2MGGmCyukkvk5IrFZG954v+Xl1U+wQXC967i6+B6LpiLZQMKzXLyJliuEqAjpk5nJmaceSpLX8zGHsN45ixJZ66Ozq0hlCh5K5SmZVirfM8jRCvkAnKuQ+DP3EcE82c+ZZkIKpMjFZ7PQpDIHMqap5DAesLZDKsfJZoWUhmg0q1VBVZ1AikihcX7HyfqY0U5UdQGqwE2eWCou/vSk+z/fBYpROasCKe52rzYFopY4F9FoiImYIibOmOSQffpeSPbB45OnkBpcBglJFXJ6ZsoTDx9hDJGAZm8dabA4q5BGoQNEGyilys4AkbAhwZQoZclNDKhdpCok+yFwmKCzMIRIXUQ2Lh/axiiUAFMUFIWZx3MJaQxuGhHB5/G1THxRe6b5baHR6UBZDHixoagEJnqMqfFmYjzsuT5c40+OWbYNRht0WVEYgxSJxeaEw81FDkbThhhhmnLYUwwObz2lEuz2e4rCQMpcCVVqiBHrLVE4rl8/p1Aluixy+Nc04MceYypG1yNVRChBXZeIGLD9nuQk475nuLxBlxJtKrpxBFmxOT1l+eiUaXfg/OlTDtuJabCgDhhTMfUjB29Zq5KbbkAoCXGYCb+R3aFnHAWdTZwsLSaWqGqRmQRyhdwblqeS7VhysqwIfuJ6cjxc1QzDxKox3Oz2lHGBKmtOT46pmgZQbDZLlk3NEB6yXD3iwc7y/MOf8KBd4tcNdTGP7gU5PVjAMPQMfc+9kyN+/dd+NYPDQmAbu5w8jSDJvCIUImcfNVFnd0OIeJVvdkkkUNm9cMtOMD7l9ZDIIWPxEMDmzm5KFlU0LJY1R0crtvs91iVcD74P7F5tqaggSpbHaxabGqMih+sD0SpenX/OR598l5/76jdo2yWmrBjsxNnDJ+y3N5xVNd3hwMeffMLDB/cZx4lnL87RxhCiYrA96RARInF0dESMnr4/cP/ePS6uLjl0B4ZxRBlNcJDI6PjGj7x6/hqWC7bRslm2iGmHvn/K3sbMK5oGyjDSDzvsOOKipa5qBttjVMXN9TV2srR1y/2Th1yrK3784+/h40TZLnBpItjAMI2cmLMsQhcZLR9mNtDucMNJecowdTSlYfADzy9e8urmOVOcqJYVTuRONwlB0hBw3AwXIOD49IQ4wyJuAyXBcnrvBNIRpCyudN7RNBVlUaLLnMkjk7hz26k5lLM/dAipsh24XbFsW7rBUZpMei6MoVitiM7jJkv0kcEH6rJAaYkPkbLQ+JQx7tM4IeeJl/cRH7KYUmudXUt+AmIWII/ZaQSSzbLhsMssDEReKcQZaKa1oVGS07NThskiJYxbi1KSsswOru1uz2q1wvtEiDJPgwsQskAokxM1NUShSFLi00BVlnlS0w+ZjN0u8xqrJCMeyGev0vPBrhRlXd8J2UvtGYaJZnGCNgI7XVPVe6LasTkzGJ1t48u1ZrmWKJVygyCyvlAIScrhYciZ6yIFBMTdyuc2cR2YJ1WCL71/xJvvbJCmoLMVq/YrfPr5xxw1JxQ65tWYc1AlJnvFctFitLyD3IXZdSmEvKMlxxTnwiI3rPm6SjNhVmKkREuFDX52lc5GjRDnVfHsjWYWOac028TlTH/OTezRSUu5SHT9C4xUKEaMapB6xNtpnnItKIuWWBiMKNnvb9BSY4ygrgqkhICm0BHnJ6T8j0QkK6NEhEiS2XEiRA678ymSfEBjEChapdFCY2NgHwfGZLEy3I3UCRkxb0PIbzCR8lgtCJJQjDGQRCJISVIQisQQRnohsEIQk0Tp2+pUE5E4YoYXdQI95ot7CoGIyXHupkHpEpfGPIZViqquaBctQqRstUOgqwoRHcTii+tpLteFzpbgafKU1YRsTpDTnhRGhu4G33XEcSS6wLPPX7BeLzg7OaZujpBFjbMjh8NECJBiRmTHmPUF0Y8kkYs+bxNu6EA0rNfH2HGHEgkFOXSwiKSywNQlbppw40R3c00RE2pdIYoFUijsMCHLAucn3BApgmLaXvH0s6dMuuDtL73F6uQ0F23acPX5K56dv+bRw1Pu3WvYXktMWXNyfAIRegIXnzxFlhoI2DAhVKLb7iFZqrLg8UmL7Wqqpef5taUbQRSXLKVhbBbUhSB4z/kusFcLhiGxKgq+/Gjg8GDNcICblzeMQ+DJO0/42te+yqHrePXyJW7V0F3c8J/80jd4/kbLf/6tX+affv97/MkPzwkSokgMdmIYBvwwcnS04ld/9Zepq7z68UKw8z6L4VTWoGg5hx/GhBEKM98sso5QzPeT2SabAkrqPD+RAjNDrrrdwLJdsj7eIEuJ8479/obD/kC/75HCEEaYDhO1rpFC4WJiP/Xsnm0xJrsUjKnwxxO//9/9P6iKkieP3kEpSV232RWCoB9HxmmkaRq0VtmSKeD09ITXr1+DgHEaUCpzPdbrNRfn5wRv2e/3SK1wbuLi6oKmXLLbD5ne2d8g/RbPDhYSrQynp6f47sD9B49ZDVOeoF1fE4YTxkOPN5GrmyuO18coqTjsMuPk4vKSzSbx4U8/5OLqHKEjK5Nx9Sk5VstN5mRoSXfYcXR0hCdQGD13mpFhHKhWa7b7HR9/9jFeTRRase/2qBCJwrM4aTl59AZjOrDZLLHOcvnqBc1ixWQzpyMzOxxj37Hb7zk+OaNta4wxWGc59APMYt+qbmercn4/IBRJCNabDQDBSITR85oFjJJIH0neIWN25TiZNXRCCZTMeT8xRh49fsQ7736Jos6ZQSFFTFliqmx7tuNIjInl6gipSvb7AxGBtR4l4Gi14vrqht55RILRz+wNlQ/Wk+MzVuvVjCRIHC+WCCl5fXFJYTTtss1C6uAZpwkhNaassxYiQMBkcJ3SaA1FikwuEoJEmZbC5Kwan9TsUrol50LbZI6K1AVy1iIC1FXDclmRk5ET7bJFSk8UklIrrPU4FxEq0m7qHNaqMtKhNDoXPVFg5veov9Oj5B+tQCBFLh5DCkhAa8EHX33I8fGKKGAjFhi94MHpfUJ8SfAOQUuSDUZXSFkxdE9z2nf4QnsihcgcrPlr3a5vbh2EUuTJjSChBRidxdAiiqw/mim3QmSL8q1iJQethllYm58jZZ6+lKVhtchhnCZZEoFFK/DRkESkUAnrwLodKW0x2mBDJEmBKVqUgH1/wBgQyuMi7HYj3n8RC/Ifevy5LlCmPlAqgdLZQVFIDRFUlBRyQRULRFTgBUYVoDweiyUSRcCINPNLILoIWucXL5GD8ZTCBogpUElJoxTWRHo8JmWlexcSow9oD5UqETFX2VFkcWMSOdExj+cVQSdSUpR1iZIapfKusyizgr2uWoSYEEVBig5ZlqQ452EISUpZHClUHn16r3l+HdErx/oY3NST/ITr9wzbLcHlA2McOj756GMePrzHV74iseslSgS2wyE7jKQiipFhcOzGgcdvJILL3AjrBkgBpSuSd0Q3EaLPRY11yEpytNowbV/TXV1hlKK7PEctFuxuLlm0Cy6ef44dezbHR4x2Ytp3FCbx/PkLQrGgKCvauuDi1TOW7RqnJd45xsPE9eUVVZGZKaYwdIc9hVRUq4YnD+9zfn3FocsTnsvLC6qypAkGkQShGulVzdP9np6CR2ctVVlSagc+ECdHVVcsDEyjJzjJi+BxsgRXkFJBF27YHl7zh3/0x/zko4/Z7/dYYKE0bz8+4ltfPuPtv/Ql4pMV/6v7Rwzd/40fv9qTXCTYkeBGmrbkl3/5myyXLSl4YgSrDZejIyiZ1fcJCqkpVT6sCqXvckxuHQOCrLH6Imr9lgSab1oCGCeLc4HFZoEWisP1lqat0EKByy4clQoOVz1t21AftwQ/4VNCU1JUBf1+x8nxCbvtNZ+df8Z6c8LJ2X1SCizrBSGEvCIBlsslbhrvQGybzYbtdktZlrlDT9k6GuZRfVGWfPbic7a7LXXbsDnKsQZ2Ckx2wgeBSpZNGZiKQEqBi8sbNpMkbXd0+wPHIaGmgJwcw80OQiApOHR7tNCkmDg9Ps1rqOMjPv38p/zow+9TFCJrdKxjuVA4FzOC31rSnPg7DT05R6ejnomuWheA5PLqgnbZ4suCr7z3Pv/8//p7OSmnLujSntA5VicrXm1veNJmwvHYdUQSk7WUpqA0hoRkszlmsVigC4UdR1KKs9Azd8QpgbUZox+cJXibQ/2kJMbsklqvWj77/CnWhRxiSMJbizKZE3J7GGiZBbM5CFDx9tvv0i5WeaUdE4VWaK2ZrKM7HGYHk+aw2+OjpG5ayqokhERVFmxOzli8OudqPzA4hwtZ92fmacrDBw9IwdO7QFMXvH71Ch8FpizRpqDQJYvFipBkzihSFVLXSBL9MBCDy0h9XeQ8KKURqqDSBZAIbiIET5qnF5HIoevyJD21GJMjMLwPjEMu+LQuUTKR8JB6nC2pqw0yPsfblpgMRu0ZnKAoDVWtUcoRkSit5ilGzh2LgEqBMJN/bwsANWtBUsorl+OThjfevY+PGmUE0XlG/yrr/cKIFmuMqRFSYagYh4SzIRdbKQc4KpmdMMyNyC2MD241MPkeYJTKqxmYnZtzISBum8/A7YJKCDlrUARCqJwvJX8250fRNiWr45rIlEM4VQ53PYxdbjhVYrvbc+gmlgtDu1ggifgwoaMnBDCmIckBrSp2g0WkBiX/9GXHn+sC5fUUsFXBMsJKFaR5l660wQiNCQonoXeeV7vXTJVnajxKS1KQeSriEnIAe4hIHTheapIOWKGYXADvqZTCBBAhIlOikhKvAr2KWAFJa0RMGfsbAlEm+j5h6kjZRNwE3ucOV6qIiInd4YIje7jbbeewt0TwnqLJUCwRROaexNnONxuNhVCIlLJjoDBsKemvDwh1jewPlMlzuN6SUqQoS9K+w009VVFwfXnBh9/9I85ONtTLkkTKo7sEh75nf+ipj05I4XYUl/AxIVQGKeWD0eBTjg6PISBjYry4ZHj9grHvUMsGgeWTZ59w//4jGK+xhyuKeWozOsvF6yvKUnMzeG6GnvceVbx89gmLeoXwkZv9jm1M1EXJ8WJFYGIY9sS0ggR127AfRm5evoQI67rFuZwITdKsVycUyuCvdty4kU9fFkxKUxUFVy8sGEllAvfWNWrK8uNr67i3MryxXPDZdc8kevbDwBAEqtwwWIu96ShMRRkTFs9//p/9Ve4VA/12y3LxHrKo+Y0nJ3SXOz7aT6RxYl1W/MIv/SKbzTor5mPCC3A+sguecDt9I99Ycr2R5v+ec5IFyHmyQpqh27eFyvwcJSVCKZQy3FzdsN3vsHHkZLlGtDVVs+AEw8c/+IhSL1BohIT7T04p3Y4oBNPOIpPEek93OHD1+oJCNHz86Uf89OmH/Nz7Pz8LtnNhPw4jy0UzJ8Z6YoxcXl4C3E1M/OioqgrnXLYaFwatFZOdqNuafuhwfqIfJqZpIinDslZMbsubj0t++OIchMAlkTs6KSm1AWuRLmKHEStBNiVN09B1HWVRMowDUim6oeM7f/JthmkPqqYRDeM4ZpefyRk5h/2esT9wcnzE9eUlTdtglKYwCu8dq2aBQKGV5v79U278liThzfffZXt1nVcqGlRVkbShtx0pwfr4BC11nrBKATHTSYUQSKHRs4PO+5yUnDUpxZ3wMgSXXRshAJIYHTfbG6TUmP1r3HDAeY+aAwCVAKEyadp5jwuBQufPKwjzwaY4OTnh9flrzu49wAfLzc2WsiwoihohBN2hZ5w6gvWYoswwst0OHxSX4UDQJUppVouW6WZPStlFlAWlmne+/PMc3X8bXRiCnWjXWQdSGMXLF59llkuzpqprqnqZu38hid5SliXWmzvooZ7F3z54EjpvJ6SmVBKEYBy7HL4XBfWioSgrqsKAEPi+R+qCGOFm11OUAimGzBwpjkmDYL9PFNow2JHj1ZJ7x4biS4Zvn7yCpyNKSPwcbSFnB6USt4f7F67K2/WKIGsZ20bz/ldW+GLPZ+c77h0vGccOxYayPCYlhZcgfE+hKqZ4zhQKFqtjpH6GlGJG32cXoNb6CwjoLZxtLlCUlHeNphIg5ewscv7O4QN5gnI7ObktUpLMotlMnxVzUSQ42dS0G8FhusLLjqZcEIOhMQ/xMTDZHiU162XBss78ExUUtSm57hOFylqZyArhJOumIih4Kf8jyeK5miB0lrGWXEbB9b6joqDSGhMFLSrzAwI4E/A6kEN+LdJoXEg4B8PlyHQVsPs977+9YXViwGhqDUJPRBGzrzv6XBnHwFGlc6BWSlwNEKPEp4SUEV0KiIJ6JUjKcxgTkzMkO1JomUvrsOWBO1CkMutNZD58nPc0qDnrJSvUb+1iufLVCHJ1HAnsbc8hTFzaG9bDFesY5m41266VUojoWLU1lakZhj3b6wuEPXCvfcSLsUMGMCERxgE3JY7eeCur5qXMKaG6Au0IIdBPESgIwO5wQ9/1rJqJy89/SJgC9dEZnz37BCUD66MldSHZnV9iCs163bAfJy52PRde0+iKJAbef7ygoCdiaM7e4qc//ojaTHifWC41bWUIVLy63nJ0tkSLBdIUSBfxSpMIDONAu6w5uXfM2A989tkz7t27x8FPxN7ywVsVu23k6vUN5/2O4Bd8/YOS41Ly8cXIbk6OftUbnl4ONEpx008Um5Yn94+pjhM/+vGHOGtxIVt7bdL8i//7P+PJf/JzrJXms48+48GXvkRbB07UxPecp25bfvUXv8nm+HiOZc8o6qASxMhOjLnAkFlfkkezEGWiSFmwFkP2DciYEGSBd0Ii0i1vI696SpEF3UiJtY62rDFli3WeXddhdWLqRprFEqKkrVpO769YrA1SlOyGLgsqfcE7776NDNBvD8ioefriKf/sv/unHK+OaR+9lzHrKeGCx7ucgLtYLHj27NmdaDbMDI3brizGyHq9ZppGNkdrrM9Arn23p++H+e/mGa3leNXQHyxF4TEii/eOT0/RduTo+Ijhs89xKWCnicurK9ovvU3HRN/3uDFH1huVabw/+MH3+fz5J5RV7mK3uy3r1TF933F01HBxcQ4o/JSF39VMui0Kgy4kYUbSpwTn5+c8/fwzqOC1UpimxB0E277HhIb7i1MOw56+z/TowhRzCnCkaVoEWfdh3UQgImLIQurF4i5B+BY3b0qD1uVckOa13jAcsCEgvWO8eoWYeU8h5s7emJzn0i7X2OtrqtLka0oIvAsIFE3bUtcVV5evORz2uXhJEe8K9qkjxkTdrCirFTbtcdaiTEFZNRQIirLGXj6lKEsa5ygLjfWR0hik0hydPuTdL3+NzfERdhoIpsEUFbeq+ydvFpiyJsyY/8BIVbUYDaO1OB+pqpq2bRiHgWkaiW4AAmVRgC5mN1mHEJKTow3Ww2IFIvncSKGIIVCWFdqUCCHZjx5cx6JeEdOely9/zNv3v45SK9p6RVNUELdYuyPEKQtgZ/5JiglJ1mnke/ItJXdeloif+ZWgKgVvvbPgF3/tTepWQDQ8+/yC9XpJ2VhG+5KmOEPrmnH6HOsslTmmKKCsPVrlFU+MMevQZkFunp5wN0W5I/VKOa8Cc8EriKQ0I/LznCkXp+lWt/LFBAZxW6DMdurZTfrG4w3NcWLrD9g4EkaL0Q1VVWO9p5KJdrFGqjLD34YrKnNMigeMiSgkPmrevP8LvL76AdZtidHfxQj8aR5/rguUy0tLN8CihFg6roZEGCxHi4K+nxBaz/kFCqOhbBWF9mgdKZzn5uC4nmDoIASDLjSil9D1uJh3me1SUShYlhoXFd2Q+QLWJq6GyIubie2YCElQCEnbKJTJSv7gJlTUnG9HDluVRVeFzl5zBO++aSlVhXUBnxRJSUypETNaOUSP9B5UrtQRcy4Ht2F/gbZtsONE7w7s7DWb8j7aO6J39MNAaRRSCeq2ReuAjCOoluW6Zi8Cn+5vIMFaVyxrQ7lesnnwmESRD5VgUcZQNoLJTfS7bWYtOMs0ZHln8I79+UuCVNxMI7sxMAwT60EhfeQw7FBCkQS8PO94uZNMKnJ6UvHu/Tfprl6TpOTmesdof8yyNKyamskFJuvYXl1g9QJbbbje7ykQSLGjXa2hbugOW9pFi2kqikKzvdyyPqq43m1x3nB01LKRJUJfEY9hl+5xefAE53h62VG3J3x+fsVRUyBD5ruUceTd44JJexAF1zE7IW4P3agSShd879XI00vPq3DO1772HleffIx2mp0L7Ic9f+1X/yL3HpwQY544eOczkyBJtni2yeNTpgOLGCmkQiEQMaFVdmS59DO2RrJg7w7ieXeryQWlB0Y7UC1K7t2/hyk0IQb6OHGzPRCcZ1HXBBvpuwMvXwwsTir2aYso4ehkQXftuLq8ZFE0qEIz9Q6C5JNPPua/+W//a/53/8X/ltPVKcM0ISV0Q493lqapKeuazfER11dXOGeRo2C93GDHaSbOei4vL9nvLlislpRVSdd31FVFioLVAnYvX6NkYlU2nL/8iPsnD0ii4DBM1E5wddNxPOXVoreO9eqY1/s9ZlnlZO3oGceemAKvLy74N//2XxNijxAlztUoqbm4PKcoSsbxGTHk7vFoUUGyvHj2ipPTU+RyiR0LFm2LVhLnHN///vd59fwVqo7ZKdTWtKsFotD4A/zkJz/l+P6Gqq5nnsceH/LKJsydsJ1GrJswpgQqBMwrhJwW7oBGLfBupplKiUDR9z1GVyjlKGXkentDCJGyKIg2oo1C+EhRNtx7cJ+Lqyuassri6phThKWCew8esjk+QgiBlhIlFc7DaPP3Z7RGmSzQLouKvt8zDQOmEHktlGCxOkIryeg8WuXJQT7cFF/++jdZLBcE79BKUlQ1bbsgztZhYpOBmiGgtCcgcW4iJUOKOTeoNDlZOiRw48jYd/k1BVJw2OlAjAIlHJM1d6wPbq3cUmGMJsQ0r7WgrluCHLAhEYJgs1wRuUTLkG3HyRLFnhJDjJrjeyVSdcQpgx21kvOkJ08ZohSIyPyOzPZfo/L04e33N/z6X3+Hr/7CE5TWDGPDbv+UmgoRRzQKzcA4TkhTIpIkioTRFesjRdVmt05V5gTsKYQZaS+IMTc0twGRYo4muF0CG61J0aFnh5pAgMiT+7v8sPnekYW2t+LYnHQvSWitKEqJMp5SgIqGfggU0lEAi0Iy2oGpP1C2a5xTiNRg00RRLKiiJYWedfM2+/01+92BZtmQgwL+I7EZD7GgHxQvrgeiybj3AsN+15GEySPryaO1wAZHUcKDleTN05IqRaxp+exyy+Alniwge/W6y2NmoTBywphIU2pMMeBJjH1AJMXgoB88/W4k6kwQLZXkahtZrQ1VBYtW410iBTjsJqQwmMKiNLTtkqJoqIoKb/eMLhGSmfMe5otGKpJ3mWeQYt4VkkAEiAKZImebEwpRMh32dPVIWlSEYUdRVayEIIWRRVPj5JJ97FgtWopCERvJD1+/4nrbgxHYyoJZEDUkYzBFPYfimRy5Dlhn6fY7bq5vZgvfwKOHGxCC/aQROlGJgInQO0khFU1bgThh7EcOY48qCyZ/4N3HR9xbwMXTj1i1Czry1GlZSJbLInM5Dh1dNHyy6zhaw8lZSRCGDy89dNe8/aakPDrm0Qdfo1GSgoTdXuDGiXLRcLWdePl6R5CWG7vhvfs1bWE5XCRM5fFhwRQV8eY1D1WgAKyDQCCYyKreU62PsTeCOhjund3j6fQUIQQ2SJYF7HvL7/6TP+B//1/8DT760SvMquGPX17wbz89J5oFy7blf5jXg5CIKNkqR6cziTTJLHTTQqIQuYue/xFz7Bt84d364vPl6yLdTtukYH2yRIoN0/+LvD+L2TXNz3qx3z090zt945rXqrmqh+pu2wwGG7DBxkhs9oQS5WznJIoP4IAYTpCPLCFZcIA4AUVEBNggIAfZieKwt3dgD94YG9u0jbvb3V3zWrVqDd/4js90jzm437XKhp2kSaRILR6pVKqqt9b7ft/3fM/9H67rd42W5eWKxdGcsiy4WF5SVxXSCCbThsG2xJAYdoEoJYpcbGxXA1IUCK1pFlO8aCEJVIJPPv6IX/jF/xs//od/gpPjEySJtt3R1BWr7ZbFfEaSgqqp2J6tUApGP6D2CGwXIuPY8/zinNMUmaU52+0O51zu/pJAEVlvrmimmsKuKcUdPvp0yYEoudlF2jEwRTBGC8OA0TU+dfguO6UAfIosLy/4v//Tf0pMntPTGUqqrDkLESEim/USrQp2u56jo2OurjYcHR1mkbpIXF1eMJnMSDFiCsl6veHs/ByBpBSGRtfYMbAdWwZnSVZyenJE37eUVf5dNuWEaZkR7SEEYoo5bbcoUVJlyrC1VNUkU0Fr9qyKPaE0ONK+Gy7LIgcK1g1puyHaEYngaDrl4cWK3egISXLv7gPm8xopFYXRpOD2h1vGCNy8cYIQEILLIDtToI3BRUXVlBwc30II0CqLPE2huIqJsp5Q1lOU0iz3zslhtBmTv9dRaV1w9959JnW5f09NSAJvB3ih90uw3W7p2o5JU2MKjVF5LWGayZ7nEbHe56lQXbGYloQkIHq8s7S7HfPFCWJvozZFsV+Ts083z0JvpUvsriPGQGu3zGaKfuwpS4NKnt2wxoWRSb2i1AWTpqEsKtQCHjyY8DuzJd0QcD4ipSEBnpSDUmNe+YT9FENKiVGKG7crfuAP3uPVN+/R2RrpL6iM5vatBjk6om6YFjXODxQVCGEQosH6HSIOTA8WNFOF1gYRIs67vBpOOZ7iRa7Oy3PihXie/foQDUIh9/pGIbKAWUiyBR32jS77tdxexyL23z+RC85mVuBEj08QgsIUkERiOy4xusgOQlUSvcii6tBRVQvG1iJloNBTNtvPsFHSzAxVZRja4uV98L1c39cFik0dh7N6D9ExtETGMWJFQQgCP3iEUBlRLQVdD+ejY6okx0cgisjJQlCqirPLkavW4vrE4DWRwKCATrAMjsnEIAz4mFXPwxBwDpIpXpIMu73tzIfEwYGhKoscMmYkVRmxQ8D2eSx/99Yp89kJyufDJ4bIOASc80SpkVGiVQHR71vlfWHyUpQVIEqasuKgXtCtz1iaLeGmIgpJMoZZZfBWEEZPiJKikDR1QV0Zljrglon5Yk4gMK0MVVFhVLYPh+CRMSJSJPiRvtsRk2dotywvLxjHgfmB4dW7Cy5GwWUXuHlUUBcVfb/hS198BZUcZaVZbx12dFTlhHbcMq8EUPPhw3PqomQnCy7WayamZnEwQZUF58sWqTSrjeeprXlw71V2Tz/ATI/57odnfOW1UyZVRR8shC0hJLZti+sdB6f3SQQac8UPvXOPs9UZ49nIsg88WieuN57j2UhZzLgxjcQiV/8ew2Z0eOexCK5aiZFTnvcDQ8zpvzGmHN6mS1IMVIXiKgj+9//1LzFJjnt37/AbHzzkehQsmpLCNEip9zA1AUqihEImzZqBoBRRCdjvj0upEQnMPo1UCMGwR5YL8SKynszvgNx57bs3kSNXkUpix4HRWrbtligtJ7dOuX/vDhfPzzFVzXwxAeHp256ry3N0KXlwdJftbkvrB7TKk75qOiGqhOs9Kkj6Vccv/bP/iacPz/jTf/pPc/fWLZSUbLYti8WUbugRZLvrruuom5ph6CmMyY63GKmqgr5rcX5B1+0Yxx4hBF1vcU4ymVRI5ymLPNlbhYCqCi6fX3AiagKBje1xOhfp267FVY7RW9bdlvnsgA8/fMQH733E2dk1Bwc1zvncSScoTMEweFJI+OBIwROcJe5XZSDpuqxRCcEz2hHrHdtuy3ZYc+PWCavLa1TU+HHgaHHI5fKa9WZNc3zKbnVFrCOS7AxKKVE3Dd5ZQoiYIjNgpFTMFvPc2ceI0vmHO4w9203ORSmrmqw4kmw316xWa7QumficjdQNjtY6Nv1IUVTMDua8/uabXD19SF0VaAFKG1Y7x+gdUilmswOqqmF0AWWyc6/tRzarC27dDlxd5DTfuqwomxnTyZT51BOTZ7O+Juw1L9b7rGFIeXKCzDlcRzdOCQgKI1Ha0I05yFALgdIK66EoJyhlUEpQGI1RghAF49ihtdlzgzqqsqSuTLYbI/BuzNqexVHOxYkRomcc8+cQIk82g3eEBIbP6bk6wHrdAorJwrBeWYbREqPm4mLFpFEs6h03J3OqasGtu0d86d2RX/6fniB8XuVkCF7WgcU9KVjsn8lCCKpJwRe/dsrsWJGUYN1vMCIiJh7kBCsd+BYnYDNcUJQSRIVkjhAeFzeUlcdU+RyP4eWChkKrHNFCfMlmyVqmz4NKQ4gkvdevKZG1T36fMr1/rcgf/3dNU/agt71QVinJpCk4vX2C1BOUC2QETIabJkFO3d45IpYQBKUGmxSjtQhXZ4v+uMHSM5tOSULQdx2jD6R053s+47+vC5Qv3KmZzBUEQ997Hl8rrsYMX6MwdGHc5yTE/NC2jiFJPj5zPG8F0wPBvBIcNop5VfPKvQkfP9/x/CoSXaIuNSko+t4TRELvd/4hakypKao9xdAqQsz2qkJrBIHew+U6QAxoabh7vyIlydVlC7FgPpsRo0OogiQykEjg8dbhVKSQgphyvgIxIF4wRV/qUkSudJMk2oQdEqv1liFGpsWUyhhk7FAlUCdEnyFKh/MZi6oC4eHZU7y3pCQYd56+Gggu0m+uGE1J1eRDjGix/Y71esvV+Tn9bktVGW7dmPPGK3e5/EDQ1Ipmkivxe6/dpZoqNudX9EtJ2dS0o+DZ9cjkUGL9lOfnG9569QHJXrPerjmoahjA9o7VsuV8PeLCSGlq7k7nLDvJs4vEzd2Sr75+yjtv3GV18RnlpOGgmbC5OCfEQKoUm2WPbS2maPAlzI5PeHc+4cPHKyqtuHWjor14zGg6xKKmc4YQFKLMn39SGkg9IUj6XWQT8k67qSdUVZ29/zKBNgQkxkierluSc7x/9RHBFHglKAuN0BVRFiRdUNU1pRAcHB3TVFN+6+oD0sfPSXt4sRAir3f2Q1AlsmKf/J9fcjRSHjbtRbQ5oyelrEySUnJwfMDlxSVTM2U6nRAYc+cVIdjAxdNLNusVZWO4fesWz549ZVJOuXxykROddc1m3WNUzbSpsa5nejChu+4pTcG42fHhRx/yC//0F/jRP/wjvP3mWxil2XU949DS1BXeWbqhZ7Aj1jrqsqTbbZEiw66M0fuxfsA5mzOhBLQ7RwKOj2Y5pt7t0EVPNZGobWLYbfEq4lIgki2dKiSkDVx2S7bdwCePnvHZp894+PGn2DEQYmIYLJOmYrttUaJkHCJDd0FZVVRlyTh0dLuWg8UBTTNlNpvifaCscmLwaB0fP/6YLm4ZRYssJY8+ewhSMLEj/XaHsomrh0/RIRD7RAqJ6XSWE3kjBG0oy4qYPFVd5tWIyc7BnLab139922XycFEyDkPOnxECoXKqMSQuLy653AxsOsumjxwtDuiGnlt37/DgtTc4e/yQ2hhE8tRlyWAjgYApax68/jbT+REHpqasS9o2Axb7fiCGAe8Ddhi4vLigrCccHt3g+Ogog/DKGiHcy0G9khLrewpT4KLA+cB2vaZuJqhpQ4gO7+PeJZTF/1pFdF2w27akIBBFyW63oesthwdzykIw2MR8WlOUVXbgmBLvAtYGykLtibOOiMCovBoy+2TmEDwp5BVHqQN6UiIlNBWYUFBVUxSJm/N7bNdP6ZwnqBPsdmB17VjclTTNEYtFwc3bHZP6jMG6fXJ0PsDD3hEjEi8R/S+0Ie98+ZT7b9Z4At4bSHO2bZtX9ARqY1lvAj5lpEOIlkJrykJQGsXZxY7ZYYHUgN/bfpN8GW+STRUqvzf7njVmVWJGQwSM1synhm5UdKP7HBO754aRXliWc9GSm6EXa2M4PW44uQ/CBGpdEMfM7BJmhg89KUhkiphS4WNCMTItK1xy6CrRd5G6LJmWC0a/RQiypdz6PA36Hq/v6wKFGLCdp9aGskx42eN6R1PMuNxsaH0WhTnnICSMlMiqwCvJ89ait4KjecGlGTmZCe7frJnUUyZVi7OK0mSB1HZnGLaRJBS9DxgFDsEwpIzYL6DUGVUeXSAJSUiCXZ8zNrQUlBVUleTGnSlFUVBMrmmHx1TNq4QY6UeLOChIcb/RTFkQy36cSIygXsCIXoziBHVTc3I0YTHX2Ljh2fIxbx69gkwaxhEhC5qiwI4Di9Jw+3jOwWLKZ48/I/hc6GgjKVJCG4mSkbbdME5PqKcLSi0Z2k0OiYuR6ANVWXDjxiGvvXIro9aTp5SBwtTI5FEqh1Atl1eUBzfBOzpZ43SJ0S0fngfeftWQwo6xbZFJYb3l5OSEVFb025FdO+KNYVIJbh+W/Pajp2x9ya2y5GtffIWhW3F4coQVivPlMue8REshI3K3JeiSp+crLj/zTCvB8uozRHOMEZFgA9EcMcbEaquo0VxcPoZGcVwfIL3j3p0FRQG9aBgvHR6NFIKjoyOWyyVK6SxsTgmlNJQlKEOMgqgURgje/tK7vPnlrzGZzGgOjzPm3RiSkPgE7jcfEkX2I6a9cr7c/7kvrsxAyB2qfDEaTez5GBlznfawprRPOj2/OGO3a6mLirqqcX1ASoMxBbP6gPV2SR1nVLIgBPbJq/ndVqstdXHAyvZcPbnAbXumhzWlKdm6LYiENDm/+uGjT3j8+DH/2X/8n/Hul76MLjQuBAY70rctujB0Q8+0rIjRZ/dSdOw2WxCwXF5TFAVdl9eqUgm6zjGZTqhLASGy2awo6hWmglv1EfKTc0Kj6CqJnha4SrGyPas48PRiyeVyzdNPn3D+9BzbD2ijSDFnCM1nE8bR8fCTT1lf7dBG8eDBPcRswqQu9517bkJyIboHXxlD2TSM0TE5LLk7vUEKis31licfP2F3vca2Fo3kxuEBt49OuFqu0FIxaTLbwztPSiPe29z9hsiu69msP6PdtSShmM/n2egTE0W1L2DqCuc8VaU5WCxAZDG99HfZfPNbrNoRpOBqvcb5DOIqyoqDoyMunnxKYxrWfY8xBWHsaAqNFoGiyCsAkQJaG9g7/qSoiSnhqgn17JgYI2VZo4uaW7cmxODZbjdYU+GRpD3Yi33yrjYFWiu0Si+jMZoygxBX1ytAUOg8zQ3OoqsFYxBEWTNdTFFFwRB8dnT1Pbdu32TsW7T3IBVSBNpdjzEStbdIS2kIIdKuNqSUKMqSQmVn3NiviSEfikp7ZtMC63fUZoqIA/JoStkPNHVFXUzw1iIqRVRQloZZoWlqw9XGYUNAyUxn3ZvpgPTSdedDxDrPdrdj2yvq+hA7XrFYHFCXlq6vWG13LNdbjMwOurKaU1cwjCP9csT6jsXshFdef5Xf+JcXjMPn+qWcQpzdXlIqtBS4fUChFNl6HuOLMMA8fZH7Z8PLLdDvwczLl+LYrJ/NX5TRgrt3p8wPAp1bUzaRel7StR29vWLoI1sLVa0oTKAmsB08bb8B4TKiQ5YkkVit1zTNnOQFXTuitMl6yu/x+r4uUD65ThQSDgBdlFRJcbSIBAuFMQwJnM0rFCFzJLrzGc0cVUbVd9sBpWA9lYydQNWSSVmThGAy0XSuY9po5vckIUYul5aqquiGke1WMY4QdSAkR3KJUQrcXjKQf1AaISXRCTqXu95BOYbukqZ4yun0dZJUuBCxLhKFRAoNKWTNiYh7DLrYd8ovKuEcRoWESaO4fWuOjY7C7ND1DNd6jDR5Bx0VWvXcPS340usHlIsDfvnJtzk+NIRestr1uKSIxlGoGo9EVTOEMGjhKEzWkoyjY1kayknJ/GjCKw9u8ejJGcMww3pH27bcvXODTbuhVgucKnFqSr9ZspjOqYTlk09WvPP6q3zhzQnj1SUWiEGyGQKnWtFt1ujoeP1mjUuRzag4W44cVY6bU8OXv/g6m6tL7O4aqQQr67jx9lssV1vK0lDNZ0ymnnbznNMDwfxoysm84XkjeX+ZCFpyelzz+KNrXj+Z897za5iUfOXelEFJztc9NxdTVBwoZYFL0OiSYAU+erTWGGMoCrPnD2R3iikqggiIJCm04j//X/4v+I/+o/+Yup7lra94UVwKBAEZPduxJRmTRdDqcw1KjqrPf0/ic86JlBLxEm0g9jqUPSFSSMTeBWKtw2iDUALrLUJrnjx5xnbVMTVTkhN0a8swOp6eP0cWiaKsWK6u8looBnTKduX2esvVxSWFMWidYVXFVOFcBmD17cAv/re/yNnZOe984Q1ee+1+Ti/VCo0BAaY09H2H3ttld+0G6zL2f7V2bDabPYNj4PDglNmsoSwMKST6zTlj1Fw/f8jNr34Je7WkvnNMsneY3znG3ppzoUZ+89FHfPLoGU8fP6NQGU6nC0ldFZmQmWB5taTfDayuN7g+UFWG997b8dqrDwjBUVcLUhSZmGsqYog4F1Bac3V9xYeffAA6UU8LHj87497rD6jKmquH13jtcHZERjg9OOb+nVeQWlKJFTZWWJcL2b4b9jyjxPX1Gu89PiTKSuO9Zz6bYZ3D+0BwHdvtBmUKSDP6rqXdFxuzwxPaAGPwRA/9GCjrhre/8kPcun2H6zv3+Oibv5n5Mj7QjyNCSOqqYTqd0DT71bgQCBFpd0MWeZo6a3WmC6LPCdX1pETicF7uO3FJ0TR01hFiQuqCwUdc8OiZoJ4dkNAENKWpMqIgRoqqRolACBKXNM1ing97siZLkFA66zyqOhfXPoDSJaSAHwa0klTTCqXFnhIe8M5SmZwIH0JExR6Ezs/J4PfUVLAxUCSNDRXe79CyJybDtD7iaFLghELMCipjCGkLhQSl6G1ubkPMqxUf4ssmIUtk2WvBwPvEb//2GfMbUBcVN44aCrnKouAERREZXUEzlfvCYodSOTpCVxkYV1aOm7cL7txt+PCDFmdD/j3f4+qzgy6/p5RZpZbt49kK7WN8iSOY1IbrTV7vCDJOIb10Hv1eDYvYT1Pmk4o337mNtILDespuXPH4astu1VOXFZNpw+gkLhq2XUKLjigT01mDCxEZ8pRGG8lUzUAFYgBVzKiLOfAfiM346TWIkHgcBpRxGZ4j85cvlKKpFHGfwfMilluI/Q5fSWQIVEWB9YH1KGmfO4Te2/yiQKlIUWqUHBGHkkVdcjKZ4ERALQTzORASUSpcSOw2gX4QtK3DyAIfBVZIQgh45zFlkTUdTjJEyfVyAw8ESii0BGvtHjedcmEBQP7ASYo8VtUmI7l9QMjAdFrzY3/oj/LoesvvPPqIWneo25kFI1OJSJmFUOnEvRPN6UlJKyRJB05OS3RULHrN6COLQjOVislMk0Rgs7qkmJfIPW+gbhrKpqEoNfWkZDqZ8MHDb8Pki5iioawMzlm22x2jqyirIz55suX+a1NcGxGp5fRQ4dwz3v+upjHAWLBN0LuAkZqQBoIqWK7XeAdPdo4vvTahFoKTmyXd+gmbs2egC+7cf4WCgAuR4xu3UH5kc/mcTz99zmIxQwjLjbsn9NfPuHWiscHiC8PZxTm3ZoJGbfnqq8csx47aKI6bClMUdJ3DGI+SnmEIEDVR+n1QWBamZqiWQSlFiAG7TypOXvCDP/KH+TP/yX9KWTWEmHNyJJFCQ6mzYn60is56FIogc6UpRHZcqSSQKa9vCBEVQQlJjaKPHkfEkyjy8zIDul7YBIUk9olxGDm8f0hQnnpaMXYju/UGLROiKXEpYgpD6GBaTLlxfCMnx6J5+vEzbOcQUjCdTWCwdOPA4fGCelYyjD3BO8bBkSycnT3jV7/+L3h09gF33r/Lj/7RH+fGyTG+29JvVlSmYBwtKWa6ZhIJazusG9ntWrZtxp6TLCFsEZTEMKPvBlKI3AgDP3RaIbikWCx5/Sd/ADG8SWpq/tUHH/GvvvEt3n96xWefXkNwjNJxOK8wKmEE1IXCFAUxRNbLFmcFpjQcnx7iw4ipSmbzQybNlMurSw4Pj6jKihAiw+CR0nB9fcWnnz6idx3JO3wfeH65xDs4u1pysz5ifnKI0qCPp/yhP/Yn+c57HzE9POXbv/MRk8UpQqjsNjOa68srjo6PmUwa3DjmxNqYiMGzXi+5c/cBUipMUeKdo+87fIgYXVCVhkSiXhxweXnJdhhxMXHj+JRbd2+z22248+obvP3uD/L+t34LrTRFIUgycHJ6Qj2dslxeI1Pak16z8wMS3W6V3TtVBSlSN1XWfQSLNnmFQugxRjOdTrhergkRpMqF4NHRMTeOctZObqgGkouQIniLNAYhXD4qXU/wkaqpicqhlCYOy5wiLLPLTgb9ku2hDUgtkWJ/AMNeNDuQQkYzlCZPg2IMmd8kIDhPjIH1rsf7Ch8GrG+Z1TW1WbAbV5xMT5GMOQpEDFjX0nYD85tQNAaxdXm6lcR+SpG70BfA+IRAy1zw1ZWmLkteu7+gdyPXG83oRiZFRYgVt04cUo54H2ldRYqZ5j2rp/SxR8sd0xsN73zpiM8+7fFeZs6LyGdTjBEp8rQqhIDY86m0NqQQsx05wWRqOKo1Ty+2xCAIKb1Qxe6nKnuOixR7wFwulObzhqPTY0Rd0Y0tngoRLWV1TFmXuNBRF3C57DGlpCzJk1oX6YeBpijwThPxEKGQisEFgnVsxjUpTb/nM/77ukBxY8jkWP0i0r7AxkBImT75eXZBZgSUWuVUUaCzds9XEGiRwUlJZq5D8FnIFh3E3kJKXG8VRvQIL/AiEk1gOikwWlCpyOFBgTry3FIVJimiT1zvBtbO4VyilDWVEmij2LQjSSSODwxRaFSp0SqhVKIfRuoix5wbmYWKpL2ZVGSaIkIjxJizWJTgq2++zf/mz874e/+X/ytL22RboszkxGQ9zjqaUnH75pzpbMGv/OYHPH6ypfVZlKuNRhWGICWDshRscaHHRLBOUMj8gPc+Wwbr2iCF4HK94lm35MGh4lhKFouavtthx8SjsxVejdyoG9xmYDXcpkKSgkc4w+98Gnhwf0ApwflasCgVq87x9MLz0WXHzcMZOlhOponVxQVCTenHS+4czSBFZGl4fnVFSpFbpSJ0kaQChQosZrMcUV9qNtdrHHOOb97gnfKK0a45kCVFVbEdtjzdtGyvNU+VxvmWV+aGk0JgYkKXNYqa/rzLMQdRvJwiwF61bwwqKpK1iAimLvgTP/nHUdpg+4HaaEqjKQuNVqBExCVoY2ITLFILUHmNo5Xej5B5MT8GmZHapdTcLGc8bM+JRdZVJdTn+2N46e4ZdwO7Tce13jA9mqIqmJZNXjcgmE0XWOlJMnL39j022yXfffQ+yQE99LuB5BKmMoQUKaqS2tQZPY5kPl8AHdF1CCMIMeCC5Wp1xRgstx7eYzptqJTB+cTl9TVlkVkbXbdDpkRRlPuk8EShFO12Q1UqtFK0bc+z5x+zXLaYpuLO4ZyF2vLoux/y5NEHnD99wm7ZIcuSb3/ykH/znY/xoqZt+wypwnIwr6ibApVCpjWbjHwvk8KUmvmsRheCk8M73Lx1lxjz75q148visygk87KiLPLhm5znyaNn1PMC6RUq5Nj7mCLPzp8zmyw4PprQjh1X60tWyxV////wDzCm5A/+2E+QUmKzXhP3kRqT6Rxns+bGeUeMETv0CKDdbZEy05Svry9pdx2zxSKn85q8sr1z8w6PP/oI5zy9h1v3XuHw6BiBYFAdr33pqzz6+APGbbvPCQts2p7tdsedu/cyAj7tk433MQkHh4cZwJjYO8Mye0SKnCeltaapa6gMR8enXJxfMQQL2iCT5N13v0TwI9Z2KGAcR5x1lE1DcBaRKrTWDF23T1Q2+LHDWrefSgpkUrkAEAnvHTHaXKRIiXVdflaHQHyxZImBkATOjnmdnwRSgQuB5CPOR7xP9CtQix27VYvRil2I0AyU1QHX45am8kgmXK62OL9juUp88qGl6xM+5lT6lLITixffn5SnKErkgD3nEn6s8cFwdj0iBTT1IVJoynKgajLnyLqG3aqjLhqMtpTGI1KDTuCcoSxr7r7SUNWC9SaQEBiVc3lycaKzZk1qSIngs4YJAS4GRmdJomG+qPL3jfByYhL3kx/xu6co+ymM0Zrjo4bmsGMUPVYqClVRNYmkImNY5tU2FbOFZGIULkUqc8hufMqsnuG8Zdtfom0BUVOY3GDteouWIher3+P1fV2gmKJEpICUCe/DPp8ki0GFEASfVUQZqCZy9egABFXMKOgRj7M5qyEkl3UkQSBiJva5mFHY3ld5F+gcQShCFKyWI0WtKXVk04VcOZuADCPTymBM4vXFnD5kx89hpVn1Iwe6wkeY1lUGLOkKoQWqII87+V02UvJZJXMUM3k+JLP1GEGSCqMSX3rtAf+7/+K/4Dfff8bTZwNdn32CYQgYIVkcTXjw+n1kM+Gj5yu2oyGFgtH2jE6Q+sB2m9/77XsN8rhGioCPkMHOWeldFgV1XVHXkk+eXfF8tNwXAVNEpMhkUVPUlEQezGqSH9g1C56tHU3suX/nFp999Ix79z23i4aHVwP3jmu2my0XV2ueXo2IADdKz5PrETWpaGaCm4cTDB4lHbKZ0ncdxVTw4PU3KHQmcW53G0DxcOl4/a3XWV89R48Dl9ueJ0933J+VzI4PUGPLZWu53BqG3vEHv1Lxwccdj9oJl0NPpRMuGqpRMsYcypc8L9XuSr3IrJAvY9W10HR9y/1bJ9jtin/zK7/K3Tu3+fIX36bQmS75ouoQQtAFx0XcEbTPkQh7m/GL4ecL7LQn4YnMVcFpNeVJvEDuu0f/8hHNyz83xpg7Kpc4++yC1fWG/mZmouQiHCgk1aSkbAzT2YSDMGOzWbF8tsRtPUIKyqZElZKiNvuxsmIYe/pVT9PU3L59lwt9mTtV73NYmRL0ceTXfuvX2Gy3/PgP/xGUqtltrrHWoVUWygVvkSLbKKeT6T4gEU6OD1C64vyy5V/9+m9yvV5z6/5Nlu0zPvrWb7G+uuaq3/BkdYVWFUVZsNluGcdICF0GlrlIUxVstj2TRlNUGiGyfkspya3j07xmSQ6hAjdv5pDA4+NbbJZnnJ6eMp3OKIqCw8NDiqpCCTg6yOnC2htWZzsevPoqu3ZAerhxekJfduhmjpsZHnWXrH7rlzlwC1YXl9x55fU8RbWBomxIMZBiZLfZcDmO1FWFD45x6NjtsuXaOYtzWTw7mc44Op6QUuRgcUjT1Ixjz8HxEXU9ZRLAdQOT6QStNKYwlGXJbDbn+vkf5pf+2X9NoQoqnUg+sjg8oao/X/FAIvqcHRa8x1mLVAoQeO+5vDinrGq8HambmuXVOUko7r/5Fh+89x6VKSgnUwYbmE0m+fnlI4O39P1AURW0u46qqkhCECKk3F2hlSaKRF2bl59FCJFJsATabkRp/TJrZux6xn58wXzLYL+9kDh4jza5eRJSYl3ADn123iSwNrK9HFk+WhFLTZQ7bt094Pi25KA6Joaa3TjSWovyhyyffMb731oxDPGlCy8DM7NOMO5zkkjZIBFswBjJ2eMBwgHBz6jrnhA68DOssux2LUlU1OWcw/mU4JYgRoS2uMHhvKOu8nP+9M6EwxslF5d5dfhCE/UCzkbKbBZSblSlkIx2Lwq3jmEMpKiY1iUSweD9fovw+YpH7lfKOQFZUhrN8aJGykBInhDBJkk7jBRFdmU55+jHlkmpUSLmSJJxTTf01NWcthuQOq+xi7IhyLxWWkwrJIar/1BsxtEmlI4ZfuVj3t0ItV/37y1RMWXUNIqQPL0bSWkfT680KkIhcry9koYQ9sFKgIiRUmkUEpUMWhgGldAaQhAYmfNe3CBYO8FgIpdxpFCSsvTgA11tcTicUGy7xLaPxCQgSTq35uDIUsYSRMQHn109Mo/jsvjR5CNKCJTMacIJ9myULASLBKSM3L5xyI9Ppvxa+JBvXnvQGl0YZKGYNIL6cIFNhqfPl3S9pZAFoPJoOSVi7F8KsFwEUzSQRtzYIWTuaCbThvmspiwCX//2d/jo8Yrf9zrow2O0rlFqQ1033BSOSm64uJZcPe0YN5rXvvIG3/7gE1Zbw92jCTcOG/zcEq1n6EuaWnB6oLCioPOeJCz3Tmrun9aENo9fRxRCCQ5mCjWZMMbE2WWLDIJu1XF0WHD/dgPjFrvtaSaGm5OB5uYR18uRzdMrxjGws5KqmbPtO37l/ZF5U/JWEYlJ0doASlLNa5IskNojo9zDpeJLMRrkosAYg/ORmCTBDZx99AFaKk7efpXSGOKe65B+V5mydj2bNCCK/RZbSIzSGNRLToEQmRZbCc1xPUMhmJQVZ8Mar7PwNb4YMP+uX3pdGeppRfCJEDP0ryxrDhYHPHt+zjCOLMQEkiZam4W7Fm6c3mAsLTpp+qEnqsDB6YKxDXRtR0zZDRNCYrSPmB/PCYDrbLY4a4GZFJzeOWV2MGM2O2RmDohBQPJsN1f03Q6SpzEFRVFSGUOtJFVpmB9MCRi++TuPObtYshta3FmiZWD9/IrkEsuuJZWG+XxKG0ackpzcO2XoHcIp2lVPvx0Yup4UPW++fpsQe+ZN7uyEguPTYzbrK6qqYtttqSsNMmccHR4e7sFX2bbpnKURcDCfcu/mHX7dKa6erKjVJW+8/gZPnz7D+5HJwZSj+69ytnrC83HFejOymMyJQtL5yOXVFVVZo5WkLAtMqYlRM7oBH0ZSzAyRw6NjlMoBkDFFDg4O6boWay1KaPp+R1kYlNIsDg6py4JNmzvg58+eMo5jPmykQGnNW1/+Cr/+L3+JKoJUmu1qzYfvv8/sh75KH9PLdbgQ+Vmy22w5OD7OibwhUFUFVVkglc4pxyrfl0VZ0W2mzBcH6G5g2w8kFEVV5NDBsoSUmC0SQiScC+h9wJ33OXAwpexO9K4jhIQyZh84mTODQowvk3W1ktkGrk3mcbBP9I6CEHNEx2w258XaJYY8VaiaKXG3JaVIVeZQw+rODFMX7JaJ3/nGwA8UJ8iZpzCR9bbl0ZNr/Paa3fOR3bXHu0iI+Xc04fe8IV7qQhIix0bsA16fn7Xsrkr8zZ5Hl0+5uu64vKxoaqgaQT0VJPkZqhDcPjT4sAExIcWeSaVYTOc4X1JNHV/7fYecPem5vk6Yvf4lv2N6WWiEtOei7Fc1PgRCijx9vmG5sswnc968W/PJ8wueXa6BjCLIkogX+UGZ4VJoxXRusASC9Sht2XUW6/JfQhi2O4+RDeWsYd3tUNLT77ZordkOGwbbAQmbHNIIRCxwbqQpzT75+Xs/47+vC5TdqsVUWUXtnEfpgJAGqRVCpn2uAKgQsDZbsDwBpVW2+AZPuc9PiClAEpTaMDiPRKCMyafJPueCPXK7LNQe5hbRUiGQmWMCCJ2pjN4lYhJsuwEhwEWPMvuqOyaMKagnFcZM8npAFvjgaXvLYlLmsC/2rbuEJOR+3ZOLp7jPURDsbVsKhEhMpgVfeecB5+cXXC43+8PTcbnZstpGNrsNO5t1KdvtJiO9y0yNDTERleOz8+fcv/lF5nqCb3coZylrTVWbbLctNSfzmklVEL0nAlpXnJ2v2Kwsvhx5cOiJuuZuUTB81rFgw7P33uPqecvBRPPlt25wefGYg9mUjy561q1lWivavkKbDl9VHFUSNW759NMtr959QFkUrM7PEUqyOCp5dtXz7Ucf8Opbb/HZs6fcrjXX7cB0OsGnHdOF5PbNAx5+sKbtA1s3gAs4lQPLbh9rXjnWfPuTkqA6onPcPiiYzqas7IitBU8/Gxh81ioj4osfQQ5wC+llISEFaAmlyYJAJ3JXw55i8IL+q/Y28SfdkhQ9UWh0TCChQlIkwR46m+9BBCeqZhYkH332kIODOWlzhjeSUkhi8nsHocwiXS2ZHy84OtKMg2WwPUknrtervB+uKtxoSaMDIbh8/jwnp3pPM5tQVRWmzEJgSsUoBlRZQc+ec1GhpcZFh0uOZl6RVGRzueNwcczJrRMG15NEoqwalDIYvaYqFVVZsVpeUJaKQoBRgrHdMCkUx4cH6ErRDnkympBIo/Ei4itBde+IdtMyqyW+d3SrHUl5PA6PJRWRsW+pJg1+TLT9yG7nCUFTFxOcTwihEEJx5+5dUgq07Y5+HDg5rREqMJlOkFIym80wxjCOlolpWK+WzE+O+ZEf/mF+4Z/+N/Qby+OPPmNY9/jgefdr7/LeBx8yUQVffvMdvvXhbzE7OKAsK975gd/H4vCE6WRGio4QPM7C0FnKqmaxmGFHm4vvcaCoagQCpQwxOGIM1GW2to/DQF0YhJJUZcHRzVtU0wZWa5RSnJ+dZyx/jAQPuhAcHB/zha98jW//1m9RaUkMiW/99jd5443XaCYThDGE4DGFIsXE4dFRBoBFj9uTbxPk15i8fpzP5yQSXcxTUzuOFNog0XjnGEfLvltEK5WTrE0BAuw44JzLB2SIDOOQV1T9kAm22uRwVBJIjR1HkJqmyRlB1lqKosQHnydN1mKKmlldQQo4axmGYb8S0XhvX/KDri53SBUwMuG6yPFhQ6krPv3I8qyIbLsr2q6jax0z3RB3gs2uQxaJ6HImjpQJicCUkqI0bNcj1gaUhoOjmr7LbJp/8H/877j3WknbDTx+1BGDYTIpaSYlX/0DN/l9P3qH2tScX19gTJVdgWT9zIYlyc3QasZbX3iNf/2vLlmtPcFnDpaSMotyydEZiDzZIMZM3o0Z4razAScijoTcBe6entJUFQ+fXRJfWJTZr/hkjnSw1nMwPyTKfD+KZNjuEvNJzWq3ZbPu8uTKeLarHZ88vWI+aZjW0GPZbC2js5hCMZlkfVzwjigSNvjPnYjf4/V9XaAkJ3HJ4YPbr0oSiYBUkqLMN6fWgkIZEooocmx5dHv1dYBB+JeiYjuO2TQjs91QKKiqilpASJHOBxQKbyMpZL+5NC8AQZIAeOuRPiHRe9Gioi4LfAo4m+8KKWSG2xQzBAqpFD7l5ON842ik1Pkz4BBJ7C3He3KolHsjzwu1UyRFhzAlgsiN0xl/8Ie+wL/41a+zXbf0XvL4uuO/+W+/yXJouR4cWmvqRhB9fh+FRAtAaxCQkif0Hb7boRnRBSiTRb7rTYcPikfnS5TOYLGzR5/Rdh4pBKczCG7LsiswMVIpTS0jUsOtRcVX377LTC+5HFoeLUcudoFKaS7XFefrDV97WyD8kmk9RcqKXbel7Vouz56Q/IgVgsX0CJEK5nXJ8uqCaloTikAzLRkDJFVgqprnFg7uHNHoirN+TShPuLrq+fjjS3rf8M7rM6QZuVxFJvWMy92Gg+OSo5niMmjGMeWIeTRJJkLI2SlCyD2gL38vIRt1Tk9P9z3c5zIS8bv+nkfCiSfrqxzwpQ0uhLxjV5qk9hMykdd8OglOmxlXqyWPrs/48tECHRLSBSiysFEC4kWiKmAmhum8ZiEmrK8VwWaBeIgeXUpmiwWmlITgKEtDdIkCw3DVE1XCN4nlbsn9t24DEZ9GZAmvvfoKu+0Oay2oRDe0yHH/O1cFUCMEj3Bgdz1KCrSBg8UMoyXDkFN5CyMpRGJal5wPI7WpmTQzpos5cd3iZcQKi02OuzfukpLg6nLFycExTy4e5phCIThYHDJ6S4i5cOpWI5fPzjC+wAhFcoFnz6557fWbtP0OLQMH0zkywdHRDYTI9tRdu0FIwXx6TL04oBtHrq5XTKqK+/fvoZQgbDfcOz3h3Xe/zIefPUFYj9hZ3nlwj/bsmjgGfuKH/zg37p6wubjCdR4109y7e58UI9NJTQgFl+fPkU2zX0EHEhG113gJKSnLau86EUSRkEriXKCqKuazGUpnkGLf7mj7jtOTGzx7ekZpDNZ5Vsslq+U1R0fHlJT0w8CD19/kvW99C50UWgjOnz3h4cNHfO1rX8nrxyIXQGmP85dC7KcVBc5afMhcjbEfUWq/KleK1WrJONr9lFCSomS32eHtiLV2j+iPe2x6le3M5GJ4Us+x48hqtcrvKSXeB2JsScGjpKSsJ4SUsN2W1eqKsqipmwzdSylix4Gh75kvDkkhcHV1xma1wjqHELmoQYJ3Aecdz89bmqZARMetO0ecnpzSdytiGnjv40uqQuJjpmYv+wGtBIe3S+Kqp7eRotjTV2OiKCWmkpxOamIQWBeZLASnd+d89smSq/UO/2mO3gCDlPDmW/e5uLjm7NkV189nFM2W5Cyt3XHv7inbNtL317z92ptooZFqzuJgwZfevcWzJw/p2/wM8WEPahMRLbNbSSiJCzY7eFKe1ErypKusSm7duQG2w/rMBvPBg5B7aQQ59DYG5pOaw8URKkX8sCUGw6yZIGWkUqDmmm43sLoYMELTr2G8aikfNHgZ2XU5sVs4DcHTti1CKKqiJHrF6MPL6fP3cn1fFyi7qwHdBCbTMk8ApMCGQGlKjMwHqvOW0WUcsi4VNgVEzDtSve9GnAsoKQENKucQ+CEgk6bvLBpQSuNDRga74JECalMQvCVJmQFEe42CTw6hVO4YnMO5FiF1nsjInEljVMGdW69gdImRYa87ERRFjVL7sECXpy4C/VI4KWTeDYt9sRJTykj8JEi4/OzQgtdeucl68yb/6ld/k+tNx3UHj857OpHV7TJBcAEjJaosadsRCUih2G7WXKyesDi8iSBgdEFMKjuWQuDxk3PWY8fONTy4cZtCF8wryXbT44sjqmLKqlU83wSmtSKQd/6jlaw7x3IIXF09w6kjLrc9Van40mnF5fM1P/RWyd2p5HojicHhpENPpzy/3DGvFHVTYlvHrrM0dUPFQKUS69FSzkqE9fhkWLU9dRw5dDVjU7CK8KCsuBaJcuo5fvOAKEd265obx5ZRGK5XFxydzGnHwGQ+owwLoEXIsFfHxwwU2+uDXuhPQgg4n7Ubs9kss1H2Y9P/uSsIeNxnt8RCSpaMqABlEnvGQp6yJZFolGEuS95/9og0r1kPHbcXR9TJYZAUUVElhbCBbhjxCCb1DFPkw+H45JB2NXB+fg4iUTc18/mMolJ4F/DjkqPjI9zgWV2ucEMuiJu6Yb1cMz+a0LU9dVVTFBpTKKQqaPuBuppw8+SUqi7YbjcURUkZNd/+1ndZpAOwHiHz+mYyqagrxbQu6HYbFJkpMWkmFEowmUyZ1FNEMeXVV1/l9L33Obs85+LpGdZZdtuR8XpgHD137t5kPp/hIyzXG4JzmKnh9p3b2I2HLiIlCClYbzcM9hCtBNZZVus169WSZnbAK6+8yuPHj+n7gRAiBwc3c/hh6zheHBBdZn7Mpw2u75jOZrz75S/xL37j68gApRu5urxEThtu3LxNjIlgIz/xR34SFJx9dE5RVDkcMnjsmIF5QqR8uA5LAJQp2ayusWPPZDpnujikqaeMQ0uyCalMTuitakLI0K0QBYv5ghunJ0iRGL1jKlXWnxhNjJmboaTklTff5qu//4f57r/5OqaoGHvHb/zGb/HmW2+gpk1OYo8xQxvJYtNxtGglMqo+xJxoHLKQN3NP8oEmhaQpNZvBsW0HNps1/TDm3wH2gkwhiX1PZNzj5w2r5XU2MSAoTEHbbtG6QAgyVG862z87R4a+x5gKvU+WDs4RYrYXK60YxtzVz2YLFvM5bduRYmJ0I1Iq6lJhNyPeaa6vB6z1bLpLPv5oyeXZkmJSIURBO4ygYDGfs123OBWY3VL4wjA4R1XllXlwEGPAFJKj44Ki1libCMFzeKDoW0M1OWE6ryDC8ZFnt+6xdsUXvnif1eqCb//rcw7vThndmrKZ4sdzjNDMDm6xvNYsd5e4/jl1U/Da269y/JtXPB93mV0lciMi5b5ggv09EXE+F1hKKqQyjNYymUy5uF4zNZEPPn2exb6IrDfapyKnvVj9h77wFm++/Q4b/R4Hsxmb7SYXKkahjaQsDzk8KDg43FLXielUI1QDIuCTYD5PGKMY7Y6ER4uG7aYn+ZbppGBwBfAfiAYlDQKhFYQ8ekwKjJEEb1GxYHAjZaPRQpHQeXcXA1rl6jMJ9gFTAmtzKJ9S0A0dMhQokR0KY4womUhKMIwWYkQpgfche/URRLL9y44ZJ41SedyKwI5ZyKtDQpaCoii4efNV7t3/KsnKnEPh87hyvQncOJyRlEGSMdRSK15GZr+EdaX9Ibbv0IUkVzCJpCUpOL785QdcX13y3r/4JgFN7yNVY9ARdi7HACipMqHU5Ae69w4dDdHnJE2FYb0d6fodm13P+eWK8+WSzllGL7Fhw+9/44usu455bXgyzPiN3zlnNxp677h9Z4JJgdl8yncfXeGKI/7Hf/OQH/nB1zh/0hIxHBaS0hTMTiQ2XrFeS0Q5YbexbFvNauw4rgN3b90guBVuHaiKGqEESif67YYgD1lGz0xAv9uhnMc0Hn3rkOFaMrSeoVQsrzpu1lAd1IjK0O921Dry+Eli3kyYTBpW2x0sZrRe0Q2WFDMrArImoSgMWpuXgrUXh0FZFlRF9TIaXUqxV/zLl1A3gDEFzoctE2WyhijlJOKZ0pg9C8WFPB49KGrGzY5lv+P03gnPNyt2yzWjtUyT5vbxCXVZsVwueX5xRj1Y1pc7ilGxvD5jMW3QsuFFhVsUil27gU7k6dquZ9hZjNC4YAkiUJWSShSoiizgHCKb1ZobJ6cE77GjRQQY+pGlXHF4eMDh/Jh2ueWTRx/SXe/45Lsf0W926MkcyFRJmTxj55jNJojoIXhm0ymlyW4o5yzjGBj7gRQjs8mUfrcldR45JtqxRRpB340MXce27Zks5mijWV1fc/P4DkJGfHRYF9BGUpsMi7t5Y8FmucsQuWHAVAOLxZyjoyP6vidG2O12fDo8xMgM2iu1Bib0Q0dRKIieV+/fQxCppw3jssMow65tuXlyk0ePH3F28YTZvOLdr3yV1eMV3o2M40BVVYxDT0qRcRzxe9ZJDHmqM/SRqpkRk8jBeGVJWTeZvqsiREfXuv0Ugv2hEpksFqQEPin6YaTvOprjQ5ztsXbMVuG65J13v8KnH3yXsWuZloqnz57y9d/8Bj/4tS8DWYsQsyohPw+EzAJJa/He0o+e+bwhuhw6GEPAjg4XEsIoRu8QUiGkwnufYVwiIZE46/IqVO1dZl1L8I5EjneIKVLXExIRO3om0xkxOIwyeOvJQDHY7bYURYEgoxuUKTMkMeVPbq0jpgxLa5opymhiklR1xfX2ipQkMSqKomAYoNu16LJEJIEWHrREGMV63bNbdyidaKqa6aykOxypmxIhRc4yC5G6Lqgmmu1mSxKCspZUUvDa64e0bY/1lqEbiD6iKknrW6zbUtUTHj5+wvXYIqVHyS1KJJppzdEpeH/FbrNjtV5T1oYHd06o6vTisb9n12QRTDZQqCyythZnc6yLkppJM6GuDSmNDO1I5y29tTgfXsxdSXtFS1GVlApu37mDUhCHiDYFZVngbWCwA/N5Rd/36EpQN5rR9kQZKEsLQVKbOSHsQFqUlogk8MFyfHSCVjv6caQqyt+jl/v/dH1fFygkGPqIaAJNpSlLgVD5UO23Nt+8Pne+ow8kmdMmhRBIrejHIXcDKR8eMsmcOYJi7C2jCKBzArHWCkKglJJKFQyjZyTHhZtCg4zYGF4wgZApEUOedZRljqPObJKEThWvvP77McUh3q2YGE1pFNjIbnD0Y8WRmqKkIsY+V8r/lrLoxWEnpYTgkaIipqxLQQpkoygT/KEf/Sqf7Vp+5de+y+ANxsNET7lc7SibEqkN3nt0WSOVoRElWMkn7z3jjAu2mw3rzZauH+lHtwcyxYx6TomyKnERrsQB3XaH6S7QcocMNXdnhuQ9YwwMrWU2WVDqgufrge88HLhVXNNUFccHNcO45XJlM+Fxqtl1G1AHXFy1HN6AB7caKu1Y956y9AzbLefDyMnJHDc6Vq5jvTN0p0cI9xn3jhesYuSTpx2vHByB37Bts+20VglRV6hCMVE7dn3BrQfHlEcHnC4KdsslHRNic4SX11RFw4P7p3zng+9wdHTI3bt3MaZgt2v57LPPiDFy6/YtXnlwn7oqc0R7jIzDyNXlFQmo65q6rtFa4wfLHTPhVVHyiew4puTV2RG1FCjrOZrMuWp7tDIcJs17Z48wB1MKrVk1BdetQEbJSbOAmPjw04dsk+P4tTtwtcP2PS7CvJ4SrGXbXxNj4PBogSBh7YDzkfliTqENYXAolWGG0hQoDf0wMtM1k6ah31pOT04Y+j5zGELESM1ysyPaEdt3iFs3ePrpE9rVDtu1XF08Z7O5pirN3kUXM/vCZ/2TFAItBdPpFDf2+3ipDJd69vQp6+WSbbsjBY9MOk+rUqIsc+OQUl7JHpuS+cGMwRc4NzCbT8Akhm7AVDoD4faah6ppSN5zfnVB2ZRcXV9yenITY0oef/oE7y2SRFk3rLerLKAvNUomjIRmPiday/F8Rlk3tMEytC1tN/InfvxPUOmK//If/l0m05pf+bV/yVt3v8jR7DAn+QZPM53gxvGlULSu87S0MBqlpqTg6bsd27Fnu1kzmy+yo0ZrBHmySwys1xvmszlFPaHrOzaDRZVTrPc8+vRT6rqkMGVO346SfrRE4MEbb/L8o/e53ubv9+/8znvcvnnK4eGCRJ7ahRD25Nr9vbp3fviwQ8lMPC2LrFlbX19gnaOeHtDHHGrovcsTjJQy4j8JvB8JPlAUJUorhBS0uy3eeYrCoJShahr0HiE/2mxv7ofh5XR76HsgsdlsqesmJzhHj7UjIQRChElTI1/ow1LWh8Xgub66yGtsWeJSwDswRlCUan/IR5QU+xV9RITIYt4w9D3OOUKK6FpTNZqqVEQRWC17hEvooBh6h9KaGOEydTiXUFLgQ6CY5KTloixQInHVXXG0OOLdd9/m8vqCrt/go6MsKmIsuDjbcH52SRwDUSWKTrFebgijQekMgoshEVLAFDl5WiDpfc55Kqqapp5k67+3SJtwQ0tjIpvOElLaJzJLQO1hb4rTG0eUxrIelnz88RlHN2uG8RkUYq/50cRYYorE2G/ovKbtPIKBrh+ZNgVCWlLSjEOHJmCDJ0bFRAfa1iHLQKMzMft7vb6vC5RYSZJOFFNDOdVEEQhjpkaqUub48X2elPCeGBNWyXyAR5A6i2BDiEiliCErzUVKlFXuBlxwyJTAecqiIHi/H3NKklREEmPwqARaaZz1uDFggyOJRFUXaCOJQuXPIStef/0PcOv221kRrkQWEEZNb7NCe9eNmcVS5VC5F1a27MLLe+vMKMiaD2TOaBC6guiJ0YLJYVLTRcX/6j//E7z6yh3++S/+OtJHajRxSMi6oVAzjFKkpBh7R9ta1sslY/8cP/q9IjyvNTIYP30urtjX3zEm3n9yzdGB4muvCFaXp4xii8OzuWpzDs3MsHUZ6e+T4r2nK6b3FXenQ7YGSrBuQoiOo1PFLAgu+wqzkNw4bnj/4ZY3H7QYpblxnLhcQ8BQeMtOl3ihYdjhtgNGGVTlmYwKHTW6LJiVx3xydoH3gmK6wApBv9wynZUUpeHk4CafPv2M0JfIasHzbaKYaaKAohBMZzOcc7z11ls8e/aM7XbHF77wxeww0Jq7d+8yaZosLk1gjGEYBz799reZzubcvn2Lvu85OTmh27WUveeOmvDMddTGcIeKOQo7DNSVgN3AopkRrePZ5prD1+9lpX1huHV0QjNEwqbldy4+pT5acOP+faQxOAHetXiXkHVJU2n0rGK9WdO2O+q65Pj4iOdn52gNs1lJh2UxqRjsyJ0HD/Ah3ztJOtrdlmEYuHnzJlVZcr1cUlcV0TumteH45IjZfILWgnt3bsDpTT75+BHTZoanRxVgW4ezQ+ZgkG2JUgqayQStJVUxoy4NpMTHn37Cxfk5dVUhpGAYO2YHC4qiJrqI82PmBfWeuqy4vLhg0y5JKuJHR7SCiW5QhUQYwWwyy7MjIWgmDX60lOXnYX1aax7cfyWnErc7pMjhdaTIrm25ujaUWmCA6dERu/UK23XsVmuCHZg2E3yQ/J/+y39EkpbVbsmyFUzHQ96690UOT07p2y3OecqiRDUZnmhMibXjy8nRbrvNqclas1hMkELgvEeZAikS69UahGA2mzKZzimaBmctSRmmi1NcbwnB8eTpBW+++QYxDvkgEIoYA0JJbr3yCpeffoL1HUaXXF5e8sv/4lf5sT/+RzK5N+0ntvspyDCODEOfQyzLkt0uFx7bzRZIfPLocbafOgmmwlnP9fUqT26EpO+HzOvYr0Zf2HJzw6WQOk+hIUcRKJVtrlkvFbOA0xTZFBA6Dm/OOC6PwEuG7YCSGju6PP0GNps1WitiyinIYp8vo/SMXb/FOktdKLbdSEwarfOEJUXPthvxLk+6qyrnBk0nJkeWdCNGkVfxEsqioNRjLui8o6oyGNMlEEpTyGz/Vcbv3aI5TiKnSSSu1ld0Xbt30kSsS7hoKZoJKXjsEAguIHT+jP2uYzopqWZgx/CSo2O0QYlc1HVdR4yJpq4py3LvchqQsUfLPB3yBOpZhVSCSV1webFjPqtQGm7eKXjnS3OMfs46QjXMqaZT0B0ohXA942D3ZhRBqQsoG5pmwq5vEXLMHJrgUbJGmxofHSEE1mOL9x3EgJcrfDj5ns/47+sCZXGvQJcJU2p83IPbfC5A0JLO9iQEPmXEvCBrL5SUWVORAiGN2cIrCkIc0DLb7kiBsjSYqAje40PEDQNFXeBcIKhMfdRKZ/5CiHu+JyCyLkFXBh8iQ+dQe5rlrdtv8oUv/Shib1GOQjFahxFglGLSGIIL+HGEqn6Zc/GiJkgpZEtJypHxIgkSe7AREqk0IlnSfkybhKAqBH/0h17jtEj89//9t3jvY82Nw1uZOrp1eNdjrWW5arladoSUp04pfc5jyV/W59baFzwAEkgirx/CYlHx0TDnw/OO24cFRvdMjGSIBd0wsNk6VF2zmJT0q47vXC04aFY0TvDsytLrBlMbrq3HhgWdaDmaSHYXWxgNgwdTSFxxxDieoQugEiwqxdVDz/0jheGMzkX63jCrNMTA8/Nz7r1xn85e8NHDa06OS0o5UroeZwVIsLsVb732Ks7D4/MddgwUM8NbX3ibJLOj23tPWeYHu7WWf/2v/zWQhbHGFHz40Ufcu/0j9JsNk/kU7z137t7h7t37tO2O7XbLYrHgstvwweNH3Lt3l3K8REvF1g8Mg+PVg2Okj+gkMELy/PlzQm0QpSHFSBwdad3hO8enl2fMX7/F4ugQZQwyJESh+YEfeJvBWeqyxBhNlIqu73B+pCg0VVWyWDQM/YASgls3j9BSYr1DyMCkrnKAnFaoacOkmnF9dY1aLGiKvSC4LJnNbjGZNCwWM0DQihWzesL8sGY2PcTHEa0FrR+IsX5pZRy7gdIUpOBIQqO1ycV/DAQ8AQvKM5lrTuc3mR7OWF6t2Sx3nJwecHR4SEyBq+Ua5wNSS56dn3F0tGB5taZoFGWjQQmSj+zanuVyy73bpwQ1gkhsVytEknRtx2JxxM1bIxfvfYsQAtbbrDGbN1xvNxzOZqx3W476gW996zssr7foSuOixw07hDE4BsZ2lyMLtOTi6hLrHHZo96RfIAX6fsgMHZGTiqWUmaZa1bmTV9lWW5QlEBlHizRFBiU6RwjZ/dTvNqzWG8q64uT0mN3TM9wYOXv2nM16kycgSn4+GRMKU02oFofsHj5F6BmGxNXFFd/4zd+mmk64e+d2fsbsWxGlDXVVAYFut8sgO+c+p5YKjQtZhBqTQBU1Eclu11JUFUYXpBTp+5F+yDoRrQ1Ka5TRNIdw49YhtoWr5xs26222YRclUit2bUdV1RSFoR+vGFfvceu1YxazGxyII0pxxLD2PH10tU/0zY4Ry8g4jmR2E5nCGiOHtaGzPRnNDZNJhbeRccysI5U0Uok9sycgo8KnQCEllkgiZNRDTCwOJqA1PgaMKDFKUghFEholwfY9daEYXRa0SiVzdMS2pSo1fXDIBGWlUQpCkkiVC7nDowNGa6mbbCcXEZq6orqlsbt1xmok8N5SVDVdN6CURmvJOPRZ4qA1zg5U2vPG26cMwVIdSmQhqZsCOwSKSc5FeusLC77whSOOTysW00Pm8xkxBZabS9xVy3x6RKUPiOIahKAqJc51VIVAC8O0VJk505bsdhbTWKzp92eHAbWilDWbzjM5bF6eJ9/L9e9VoPz8z/88/9V/9V/x3e9+l7qu+ZEf+RH+6l/9q7zzzjsvX5NS4ud+7uf423/7b7NcLvnhH/5h/ubf/Jt8+ctffvmacRz5S3/pL/GP//E/pu97fuInfoK/9bf+Fvfu3fv3+TiUdQKh8H1ES4HyUOoKax1KKSo9YRhHBAGtJYXWjNYSfWRwWZg6OouSmXIZbUKbAk/uuMYxV+JIQxIRgccOA0SFQKEQyJQxv9ZaEglZCIpCMwx5ahJcHhta7ynKBa+9+kNU9Q2iDIikSTGwGR3IwNwYSpkQ0eHtQPQqTzc+/+ZmPkLMznuE2qdbyhezDZJuIEgEI0JohMyFVAqRZx8+5v/xP3yTszZhGs2k0BiZybuSiJRZZ5HS3kvL58XJ7y5UYoy/59/7BJehYLmZs9pY6jJQ1VOUtxzPGy63GUZ2ODf0YSQMkeDg24+fIcKCL9yHykxwseDhZy32ULBWdzDxPZrW8AOvTbkyPatrhTuY8/DhljfKEpeuWA0PSNsRKQYW05v020sCgat1xIWBWVnhaLg4u2Q6Kbh7I4/blzYxLafs2oBoJlyuWj4+W3H6ytuo07s8/ey7vH6i+PZ3vo3Uki9/4UuEkNda0+mUcRy5efMW5+fnHB0dUpYFN2/coCoLRikY3YgpDPP5nMIYugTbtmXwjt3Ycff4hIUuuacauijRUnCxWfIDp/cyIlxKnPWcLa+Z3DrIuPIA06B4FHoWRjK5fUhxOAMlMz0lBLRW3Lx3SNe32QWkNdZHqukErWc4P6K1pGkOcm5QUbzUHFjngCz+rSqBkIJxzJk9r8xvMWkagvNMmwmb7WYvDJZIlZjPZ9QGjJJMD2tkMrTdlhwN73HOEl222ZZGY5TEjyOLpn4p0lNaUDYFxzcO8KWnbDRKJ6KEe/ePKF+/Q/Rpn7y748GDE5arNVIX3LhxRNu1HC6mXF1eUxc13npMVbLatvTbAXW3oJwWeDtQasE4jLS7luX1knu37/GdTz6gKvOq1BQVRzdOsdbju57r1Zrj7ZbLqxVSFei6JgVBPS2JSFKSrLZLikJRmYrSzJFCsllvMp4dwfnzZwAUZYEdLZP5gkJJ+m5L1/csFotM/Ry6bLPdE4K9t0yn0z36viCGQN8FJk3DMIzcvHHMerWFmGi7nv/hf/xV3v3SWxwfH1JPGvq+z5oXO+JUiagmyJgoZeZ6PHt+zjtf+QEGn5hUZg/t2md/kfAhYaqS6DxVUZAk9G2LFIpqOmXb9SQU2mguLq55+PAJpzeOM45epH3xJXPhI/NkQyqLmX3C21/8Qzg/sNvc5ju/fcX6+ZoQE34cKMoCyHTdYRc4f/qcs6fPOTz6hLIquHn0LvfuvYMPFhEVUiu6vifsaeBFWeynPvnZuNrtiNHtqc2J0Q5ooZhPSrato6oNpWlYbld0g6epCiqlsWlgNp8SU0RriUuRsixxPoesWgJKyc+xAilQFBKfEs6NVKpiOmkYe8d8UaO1JHiJd3bPoMoYi+hGgnegDNN5lXV50wWNrujHliQih6cNo/WEIZJiwLoRHx11XSGloW13ODvgXKKpNHdeW2AWEeElVRRIGUBbqrrk+LTIzeXrhxzMDclr1uuOYdiy7SyqLJjqQzarwNV6jdJbijqRpCSOgvlE4rTdN82GZjrh6nJgtQ0cHkM9E3Rbj9YSZRKnNw4xyvx7WY3/vQqUX/qlX+LP/bk/xx/4A38A7z0/+7M/y0/91E/x7W9/m8lkAsBf+2t/jb/+1/86f+/v/T3efvtt/spf+Sv8yT/5J3nvvfeYzWYA/IW/8Bf4hV/4Bf7JP/knHB8f8xf/4l/kz/yZP8PXv/71l86I7+V65ZZis6zZDVl17kVCxizI6ntHoxWH1ZS221E4RWEKZKkYbEQkSCJC0qSQuQ6FyQ/zIKHSFTJEAhEXQoa3pr37RxlwWSyoCoN3Fu8dRmtMXdLveqwPIHPgWl1p2t5RV4ecnL6SM1eEwLucDuxDjsouS41gwGiZq2Tn9omd8IIgm8SLB1d+qIs9gj13ix4h82RDprx+kiKBCMgCLpY7tv2O6BS7tWQrNdootKqQuqGZzThWlmFYE1yHjJFa7z9rSlxtR7JMYB9UB0DeKdaq5P1Hz7n3xg36ZcVVd06w0/zQ2nbUdc2intLuRiwGq3pCgEcrx3W35Y++fco0bpiVgmJa4EdF7AxSG5SsWEeJEJaivWIuR0xxSCgjV5ef0sxOGCM83eyoJZQYtrstoV6g9YQP33/GndsHSF3z8HLJm28eIPuBdtsRdWDoBu4/eAukYsuUb37wCdvdyOizY6JQmZ5oreX58+e8/vrrLwu2J08+Yz6f8e1vfZvdbsODu3eoqorVdstgLSkl1us1lxcXhODp7MC3Pv6A7z78kLu37nA6nfBpv0MV4CSs/cBxMSMC19sNg5EUZUHyAdtZ6hHKJAnDSK0FYnRIVewjHTJMbj2uGfZI8EIVWG9RSZFcRClJuxspVIGWGm8HqrIkAlVZ5BGuhBKNUjofwC9osSmwWEyoygpdhP0kLe+wpRhppgUhZiJpP7Z8+uxj7t25QylrQnToPa2yaqaMfYY5xZgwJouNvQ+sNyvKUrFYNGy6NaXUHM6PctBgTAQ8TVMzqzVFWdFUBcOYO8aj/VTocFpjpOHs2Rk3Do54885NrlZXIAJC6AwlFDK76hB5Whk8QzdQFRUJmTVmQ+Do4JCVtRR1w2a15uriOSFa+l1gtmjQSeynBSXp5gneO2aTGUezGyip6PuOsmqoqpLJ4mivw7HUdY4dkDIHQ86mM6qqYhg6RmvxPlHVORsqhIjWGZTWXl9lLcrLXCi4efuU1fUqryPrmtffeIBPfj81c5mvUhRUzYQHb7zBdHHA9dUl4zBgnadoJhQmEF1HMhVCFSipkVpmMWsIWJf/jJgy67pQcOP2TSaLKednl1xc7TBScnh4yHq1Yb444OT0BDu2OVcsgdYlWmUXk5SGsw8T3yk+phXfwImbpNnriMtMbZXS4P3eDZkS0+YAo3+AYWwZLlrWYcuzj7/B9lpiO4mSChUlhZG4CMGP9N5SV2WeDArQpqBpYOwizURwtU7IEkRSFGWZ07f7nqo0iKqgMRXGaDYKdF3g3QjJkRKsNy1hSIzOY4pEqqGsS6SQuHGHSBEbEnWtyekVHmP2mImUI/sKKeitR0mNMRqSIPjsegjJ0XtPiom+t/Rdn9EWSBaLCmv7HAMQAoqM3B/GPn9vlaTUcHpS08wVbT8ghKCp6wysa0q0qmhqyfFBwf27Nzg8bOh3W3zweCdJsST0iTVbTo9vset3TGd3CX6HUIJQSrxwSFkSvGU2mWCk4ZVXDulGRUjnKAwH8wVSRPphhxLgxjEbB77H69+rQPnFX/zF3/PPf/fv/l1u3LjB17/+df7YH/tjpJT4G3/jb/CzP/uz/Nk/+2cB+Pt//+9z8+ZN/tE/+kf89E//NOv1mr/zd/4O/+Af/AN+8id/EoB/+A//Iffv3+ef//N/zp/6U3/q33nfcXwxssvXZrMB4GTRsLtyewBTznGw3lNIwayaIpzDB5/ZJyHgo82CJ1VAzDZjYwqM2U9dJEgSts8rD5EiujEkIYje54dZkCgjqRrNdDZhvW2JCZCK3nmCkIDMaxuj8C6zVeqq4vTGnWxX9j1KFBmxnyQiQVUphPBAoCgqImLfgYjPBbIxI5VReq/mzknH+WGVUESS6xAm2/eyFilbo6Us+NoPvcP0//zf4cZIJQQpCXxf4KLEJ0UQhkpH7p+WzHVJU2oIlnWX8AgO6p7OOa67QGcjInmkiBnjPVpevdWg+y2L+cj1NnK97RiV4Xq75V5VEaVhk0bONtdsLZRNRWEqNJpNl/jysWY2CfSq5vRIsVofghp4voJvfXrOj//BV3hVt2ALnq4SevoKh+4ZMuTVz/bigsWNBaMN2DHw9PmKoTWMKfDwUYcVCmkE3bhmNzhqJPNJjRssn372lLNt4OMnHddty6SZ8Wu/9uu0/Y4QAr/+679G13W8//771HWNMYa+zwLmb3zjG+x2HXXd8PjpJZA4OTnCOXj48BGjzSCrd95+h0+fPePJ5ormtTssR8f1Z2ekqua955fMDmc82S35+PknWCMx8wnFjcOXbJRdsFxcrdBGZadJArsbEFVDlAmVcibV1eYKxD491/d4N1Ls73UfYAyZClmYPaBPZA5Fcl0me+5FrFVZZ8R4jDns0BiSdAzOowrQJhfgfd8TpSSEhCwUQiQKI/Fx4OMn73NUnPLK3deomhkpWAhuX1wLuq5FyQmU+fNNmoZbt49R24SuI0ooJk1NjIHoPKqUzOclSk4YneXO/IR26LAuO1z0yYztZodMkrrWHE5mHC4OeHpR4WPCKA1OE5zFVAXHx0fcunOL66slkrDHBiQiUJuK1fU1wXlKqSEmhmGHUZEoIIwt03rK7ZMjLi6uWV7ui9DVml2x4dVbbyCVxGiJHUequsk/l6rC++ziKaqaScqrHec9oJhM5gAowT4NHeDzZ1Db7qiqGlMUaG2o64qv/r6v8mbboo2hLIs95dTTjz2zyWRvAoiUWnD//m0ePLhDDJFEJASyHiMGIFNGnbWZY6PkPqhvv9q2lhSzAPb05jEHbs6N0yPe++Ahm3VL0yhOjg95/vwx81nFtCkpyxd6G0VdZ2S+MAVHvM0n3zjnup2yGy5pKsXJ9BYx9USgaSaYoshCaVVhbYPSp9nyDwzDQOygNArnHbYPHMxnCC2QSqD9SAgj5y6gteDttw4o6x5TJIwpefbcs7zo2W4tKIEde4KLFFoRUmR0Fhtyhk3f7pBk8KYqNNFr/tP/5H/NdvUZ3/zmLyNqw3rb4cdAoQ2js4TgmZUFYx/wcaRQAqMNq81AVRi0SEzKMksPhKBSmhsnc1ySnF1dsB1Grtw2Z8IFIICWClUahIh0fbata6URKeDtgBSBptYsFiWqgcFGvJOUlSSKHIMQ/MC8hnHwHMwXlCIRQsdkWlEVBTHUdKPF+26fVp04OTUoWeDtBOsS3TAw0dkyXNc1IdZ4FyiLBcL32LGk1iVGlITY4oOk79Ykl0M4v9fr/ycNynqdsblHR0cAfPLJJzx//pyf+qmfevmasiz5sR/7MX7lV36Fn/7pn+brX/86zrnf85o7d+7w7rvv8iu/8iv/swXKz//8z/NzP/dz/+4HiDA9FJxt+mzz9dkap0vBYDu0ECQp2bmRutYknfelSmXBrBviHj2e98LD6BBIpCowuqLQgq3dEYVESIEbHSnsw9RKwyjz6iI7eTKmOQLeOSBrFhCCEDwyKo6Ob+c9aQx46wjkjpYoqEyBkT6nve5DqLIGJMPeckS73//IMnROyhzTLPI4KBuRUyAKCULvX7/3uYfIq2/c4kvvvsF3PtmyvLrCdhua0tJosd+TGmph0d7xZFXQRYOXmhQVQmuENLx2p2C22jImiY8RF7NV8OCwYXXdMjM1i3lEoFkcz/j2kzVFMWUxXbBqHRsvuWoHVKqoS0nJQCMknzzbcefWgrcXA8/PLFeX7+NsiRUVnar4wS++xhdO5zSq5Hp5SeUC15vIrJiw210zn5b4ItC7HR5JSgHpUlaPV5Lr64CuI6+cSugFQx+ZHOSvb7KYc71peL69pvOCsqywcSRGMKbAuexMeLHWGoaBlBJ1Xb+crOTDXnN2sUSaghgV9+7e5/gk6yWU1GgUelLz7vUzLj/+Jms7UGmNLwzpZMHRdM50saCZTPEpZSqxhJ0dQMDk+BBfGJ48/BTlPfcnC2gt6SiShHrJRqirCf3YURZZLIcMCK3orc3ahKKiLkqGoc/dTIyZJJlC7vhS/jpdGkkhYfZ05hADYQ/t8iliEuADvc/Ngx0tZVVgKoXQEi0U15szdKlYrxcYoRj6fp9NlN1t4ziglWQ2myCFZLvbInXi4GjCZFFSVQ2jC3sYXSQGjylVBhnqiKk1pTL0mx0hWnQ1ZXEyxXYj9fQAkRKD6gl1XuGSUgYpRsWNg2NOTk5ACz54/BHWbamTxlFz6/iUw4MZ52c7QtrHZQjJa6/eY3a6YNcPWNtydDinW58jneWgnhCjw4eRepLzlOomu0HqZpp/P/cCVKnyn+d9QKgCXRSUtcYWPdY6yqoieEfXtaQU6dqWYh9aWFcTyroBIsZky/si+H3IX16jKJmnsKO1GK3wLrtxTGn2Fnj50p5qxzGD1KRGKoU2mWI7jgPOjgyDQ5K1FUpJFoeLPXn7czTBbD5nHEeKIqef37uX7dhZ+AuL+QnaKJwdCd5jjGHSnDCbzjnc3UAZQ2kUu27MFn7x4nvV56ydooSYCEESfUAqRVOWCJ3vBaULunbNZrvZh+FBlSy3ThbsgHE3kkLE9YJJnZ+Nt09Khl1kvR4pC83i0LC+HikrTTMxCKG4vOqZNSVSCIKDV2/fA23pe0lT91xdrClK0EYwIpF1DoOdTReI1HJ4eIhSFd12S21U5piMEBE000zHnc9KQgysVju67YreBXz0aFkwmZVcXfcMbZ+NHHiEjNSVpu9HYgzYGIgpURnJ0cGMciKIKuCDp0RTFRK9PyvsYKnKxI0Tw92bMw6mBc1EkaKkNJmz49LIZvOU6XxBXdS4uGUyDQzDwHxxk0ji+BisbUkpR0V03XWWHkjDYtrQ9wZdzqjKOV07cnRwE0Wkb9v//7h4Ukr8zM/8DH/kj/wR3n33XQCeP38OwM2bN3/Pa2/evMmjR49evuZFENe//ZoX//+/ff3lv/yX+Zmf+ZmX/7zZbLh//z7ORXQpmB0ZnI2oIEhesRs9XnoUAiEUQUOQApk01nm8bbP3WxSZqKqzwwZtUEXJ0HVowx6wFPfiq4CWGmNE9v/7xOFiQTd2DP2Ys2qUZBxGSFAqidY5o8UUhqZZMD84yb6XfaeripI4OlxMrDc7ZkcaKST90FNVVc6v0BGh2ecEZvEaIgdrvRTMij00A5EtSyGC3qfiokhugATNtOQrX36d959+QCoijx5dopSlMXA6Udw9LClFoMOgtWMmwAWPS4rRG1wSfHIR6V2Rbx0ZqSuDVJqri4EkSj7cJG5Fzao3eG3pBklyNZ88dy85B7XOZNGZqBnikmkx4fng+PDhyI1XCkItuVOVfCpnjLuBx8+uuXNUsNyWbLXDo3FaU6iBYHdQSq69Z3Y4RYZI66dsxxUH0xLrYNcGCuNxQlAozWrrKaoKjMKqgrWteHrRsVqNpP0qTaR9QCNpH8AnX+aCQAb35edgdgqEFJBa0I89yeYIBQQUhSGl3NGmmGhUxZ/5/X+UH/3yD/Lk8ox/9s3f4Be++WuQoH7jVe4eHCErUAlEEigiHQEpK2TKlNjq/k0uLq4QbctCK2TXU5YFgZwf0xR1Rto7my3kuiTFfB/HkCBJIgJTVns+S8ZjG6Pw0b4k45IS/djneAilUSKjy5MU9Nax2fU5zVln/cF82uCdZXADMUSCytoWYyTXV5dUMuez2Oj2U4Fc5IUQWK/X6LLAlIZtv6JPPZPZjM62JKGoqoqyKIneZ6uxKghdzvswjWEiphAzkj1E91KMt91umRzPOJ5odlctYp1XFgbB8dERt27dZrnb8PTqObpS2NhzfnnG0fyQJCJlUXK8OIAQOLu6QGpYLBqaqWEYBETLbFIwKUsOj2+gjGDTLrHO58DHCJPJlOlslkWuMWZLavCUhcmOGeeIwaGUQGuFcxY7tDjvKMsCKRTlC9GrNvtCNPN3vPeE4NksrzBSoMsyr3bJawMpJUWZ9SNJZHsz5E5eK41OJttxY0QogS40aT/RkSIzm0xZEl1AaU2KUBiF9p7WJwQGiaAqCyZ1RVFW+V6eThnt3lJtHaMbSCk73Kp6ku+7GEj4zMwAQsyW1mlTM9eei3XLuLcna1Mwmc5QShKiIPEi2ycQfCCESGkO2O22BJttvlHXfHyxRZmKqpzwE7//f8ty+20urz/k8fk51sOdWyeYYkdjBJPaczwtaKYN1VRT1JGhP6GSjvV2R1FWTAzoqmTXweOnv4WXNcfHJ7T9luOjCdNqyugHktB4L1EajErQZIy90oJ7r54wxiyYLkyNiwNusJACPkBVNng1MFnUeA/qROEakyfjSdBuBwqTKJsJxMDZ+Q4lBbNZweKkxuNJJkMru51jMi32oYAZKLloNDeONK/cPaYdEheX15wc1ox9zxACSEkQmdQrvGawjlF7em/p+gy/a6oDhnFFWUuWmw2ji5TGkPqs3wpR0m6X1EWFUYe03TUhwq7fZJnC93j9f12g/Pk//+f5xje+wS//8i//O//t31bp/m6B5f+r6//da8oyjwn/7atPORPh5BhSkCy0wQ6RR2eWMUm0KgjRUpoKmRQyKYKqKFT1cnSZDT8CUmDWlMRksClAHAkRKlOSgmAIeWWjVF4jQbYz992QJx0RtBLMpwu89YhoGfuRGBVSR8ysxJji5bZGqQL22SAJyaZ3bDtP0UAcLdYVWViWIikEhN9D2gQZHKZLIhaE32+BXgiPZO409xRFkCTvif/P9t402LKzLP/+PcMa93Sm7j49pDudEAwQhhBQxJSiWEgZ9LV8SxQZS6veQiEmogIlVkFZMuiHv5aUYklZ+AEpLAtQ9G9ZBIUgL4H4hgQDSEAydaeH032GPa35eZ73w7PO7nQS/mYkHVhXVSd19l5777XuvfZa93Pf131drkFKzVJPcebMvWzsNFjpOSrG+YvDsTMzVnsap7yrqdaGUDgCKYidwqJxWGIlqYymtArXxJi6Ye+ywtiC+zZCzgpBZjKMWWY6nxNIP2YnaVjqD5hmJUuJZSAlcxO2e22Z5YKv3Jtz5HlXoYqz/MDlV3D6W7ezefIks3idjf4B1ObXuGgYUdYV2c4Wy4OQOpec3Q4ZlxrlLHM7oSoFSRJTqIrtsmItDRiNBmijkGFJOhhRNDHfvmeLb528E6EG1I2XbW9nlVrBQ5/4GWO9rsRC6l7c75yVaC28xoOQKK3ble0DyGACtPDKvXEcsT5YIg0TkiDClRV60OOS0SFOzbbJix1qrciRBE5ghKARgLPU2uBWY5L9Ic3ZOTWW2NI6djvqIkNpSV3kftSyUX7kXliUtWA9t0BqkCpoV/RgTO21SO5nhBjqwIvGKUHeNGBqgiCgcZaqqjDGEIahJ4ijQGjiqEeWZeS1IU0ktq44vTGmR0J/kFCJwlcKG00aRDgB89xRVxmnZxvMZEYT1lim3tfFQUJKTI+mMaTaYesMi2M4GKKEoqodsdaUVUFezphXYwajHiuDoVdC1aAx1E1JY3J6oxEra2sMR2ucOP4tTOQIw5CmcbjKcHrjLPXccXBtH4P1ZQqTc+Lur6H6ikRIgtrRWEkSj6iqirwoUGFNUzcMRz209O0zFYREcUSe5VR1TdxO7uzyypTWwDkfFG+gByrQrbNwjVQG09Q0QtE000XLp8pLT47UEh0E9JRA0XBmWlFbSxKn9Hp9Ah3QOOc9vVr9JNNUfroFGPT7bbUmp66KhRCbNW1SgkWFGi01SZKCa5hlBSJKUEpSFG3lWQfUtXeBb6aTtrXsydamrjB14SeDlpZREsoiZz4pFslqbRtEXZNYOHFiE+KUfqgpGkNZ5sRRSJWVVI0nqTbOkOcZUmoa47kX1lrCMGgTIOtbRABOsDa4iNVoiacduJofmJ3im3ffQVVVrKY7qCgjjb1didYhYQJ1NaOMHVEgOXBgCKKkmFjCuMfqqOQbd044dTpj72iFQPgBhkBE4PzETt4IZrM5mc3QkTfGVKFkVlZEYUjaSyhrsFbQ66eU9ZxekuJdnlOkqCjGOaurQ/pHDzDLxhRZThhasqIicSHFtOJwf9lL20tLaTNU6KdQRys9ZhMvxJYVNcu9ASsrKQf3CPbtWaasa2wjyDPJfbm/ZobRiMFy6hdnAkQI/WQFU1cU9YyqMShSZnaCsyV2rlEyxrg5FRll1dDTGlSNtWAb3y7Lirkfu961SniYeFQJyrXXXssnP/lJPve5z503ebO+vg74Ksn+/fsXj29sbCyqKuvr636kdXv7vCrKxsYGL37xix/RfqhQkMaQaEdRGQSa2aSiFwt6WKTQSBUxK3OU9GUsoaC2AF62vbaWSkqaMsc1U6yRVBiSRCFD5X17KkuiJQaDFQodBUhrmVVTdKQQur2X1Q0CjSkqyrpYkJ5UDWk0JJCxF4mT3klyd/xNITAO8rKGnqZpnFefFdqv2qVX/RNC3I+g6nv4wniCrLV4hUEA6znzWIeTflWNNRBEHDy0hzBwzKspSlkGgWIQOuJQURlHEAfM8lb3xHq9FRoL1D45EtonSK4msY7QGcJA0ZMw3oajh2rmYcL8uGVufC85CANqpdEOqnxOP5Ks9BOcMRzUEWcnJYGWzGuY6iXWLn0+43v/k1OTmmt++fV89Stf4/KrriaUc7b/YwvGd7NvT49eT1NVORECtS0oKktWhQR9zaBfMs9zpiri5DigyhX7nMSIht5ywsY23HN8hxPjjFkF/Z5tL2r+3HLO670IIbwxZOt145wjCLzrtb8An5Ob1jpCSu0luxEPYqsvxrWFaG/okmcePMLTDh8hMFAp0A7u+8ZtfO0L/5u6njFORoRryxRrBwjSJSoExgqkDUjUgOTgCNpqjfH5CVVVMt+ZMRz6m7MOQ8JQo6Rfoda1pbYVZVGTmwwpFVVVLkh0UnhdiTzP6fX7CBlQ5AVVVdLr9RjvjImTGCWlbxcIf6x5niOwhKFiOOhTtQqU29UYZxUOx+bmWWqbkUYxpVKYKGHP+j50HHE622bS5LhY0cjas/8Tv/qzAkrnBcdKW3vypnWMZxMCFZBXOdN5w9b2Jv1RDx1KmixnMArQYUiVF2SFt3MIk4QojukN+lhhmOQTgr6msBmB1IjaUeYzKhGi5BrT+TbhMGTeTBCJpTIllatZO7yKcxDZCDd2TGcZDRbj/I1dSNGa45U4Y4jTHkr6kVODJaakn53lbB1gZYDUmkgY6jj2HilCMp9NGS2NGCyvUpa+hWPqyldGggCBJ2FrrZnYdsJOB0RAr+fJuVJ4afa6KD1ZNQy8D4sxCKGo6pqyKDDWEccR1jmCxJsWSiV9hcd5rpkAkBobxghk2xoOUVL55LGX4Kx/X+cck8kORVESKIVW3nn30qPLGGfZOH6aQEQ+ucFSW0ugQqqgjx5CKh1poDibV5RZSZbPqUuvzFvFKXEY0FcSjSWLQixgG39OeukH0+qEWJwVFPMZJp9jGsfq6iF+5IqjjGdbTLNtjp/9L3a2j4NTlM0cFYQkQZ+scCSpwPUFVeOYbFX0BzFlM6GcC1aTlIsOXsp4q+LOe04i5ZSycORG41q/pST2lftER5SVQeuQumnQQUDsDDru02DYs973FVwryLMZjZOs7FuDukCqmqQfo4QhCh0rcpXt7W2whkFvQBBFTLMxk3GJlhIdSYLAsb5/ibqsQIcEgWBlmDJaSYj7e9Fa0ojTXHR4RG0q6lKiRIgKG6J0iGkUzo2BhqKoMaaiNg1V7UiTgH6aUudzcpMTyICQiEpCWRlWl0IUMdZWTCZnEUKRJgqtIjYffgHlkSUozjmuvfZaPvGJT/DZz36Wo0ePnvf80aNHWV9f54YbbuDKK68EvFTyjTfeyB/+4R8CcNVVVxEEATfccAOvfOUrATh58iRf/epX+aM/+qNHsjuEFvIs59DaiBOnJkyLAKkch1ZiBnHEic2CE5MZhXOEWhMGEZUtEaFCWEGZV+CEV7pUCVVdowJB6HzyUJsG21gSHeNq3zcsqgZTVUQhlE1NXflEJw1ihPX9UYf/0drKe7j4KYd2vE7oVrnW+AzTOpRQWCcpKoNxAVVtPIEW0Y7leQ7K7lSOFM6bsgEtRbwl9XsGuJQCrE8wkBKpY5znv3Hw0F6O7Btx9+k5tayJtSIOLEEgCTVopSjNrgCbnx4wrp3WUBLtdu0BTGsQ5q1+gySk3s5Z6u/DKslF6xOOnW1wJH6qqmgQytKUc3pxyua8pMwzrjo0oB6EuEqwM8mpbcJk5yxbp4+zoSz7L30uq0evIgkVZ77xKUI3Z/nAAU6fOUOyNKCYKhItuCzOOLM559RUkOcx6VJGOuox3QyYZhN6wYC5KZkZw3/dUbA5N+R1TVbmrKzsQ7TW77uVKCGEj7cUlGXhxwilaFdnXmpbShbVFGudn+7CO8Z6xcz7JyitzXirLeOXycI7o3rZHlxbtFlb28dFoiY4ew/TDEZ3CLYv+gGed80vc+ttNxFmW9ybxMh+jJURqt1Xq7x7dxR5MSbwZpe1NeR57bV6rOdEWWcQwnsGWWPQUmFae4NdvZcgiKjKGq29b4eWCtsYAqXAWLTyZN2qrHDGa2PM5xlSRsyzCiUjTGNZWl8lXukhtUYazXhrjBo6+kvL7QodwiRmPinYzibUscUIy6ycI4xsx00dtmoIdMS0mFOXBVppT4pXAWESIUNN6oboOATlUFFI6bwqrq0aTwptWm6XkGyPd0hmW5ydnKISGSKVhMSIyqGtoCxn3hQuUhjZUJBTBRWyJwlNQC1r34ZJIkIZsn/viHyeU2Ql1hiEFN6XpCh8khiEGNNQ5DMQmnJylmPbZxkcPIqWUBQ5OeCHiyRxknqPMCeomxqw1JVvn+Gc14AREEUJKvBSCfM8I5ACpb2+jOeleBl40ziCQGOMF0dz1ht4RmHgk+6mwjkIlNcDccZRZOXCXUO302xSCfr9EUpLijzzWjvWYmxDkWcY0+Cc9K0ZHdIfhARBiLOGLJ8jsRw6fBGz7Rn5du1NMo2hygqEdkynY0xjmGtFE0RESYDBj8FLpf00kTVs7swZpn3qqgLteSkCSRBFBEFAls0p8xxjbVuV0Az27GW8eZp8tkPcG7BvzyEuCo+w0htyYsdxZn43ubOkcR+BI9s4g7Up+STgvnvnNDLmzOYOQZCwtrTfezwVM1bXDnLvibNEkUTJGlE7hJaIOmYwXCIINWEQUNQZpqloCktW5IRhQIjD4sUB63JOU1qCSDMbF6RBSmMaJjM/rh5HIXlVM0gC5NSxtif1I9WxYxSlrC6vUBYZ/VFClmdYVyNC6KVeiXae53z72IyysfTTmN5Qs5asYdgmSS35vKTZqkl0H0zT1kU1kSmRsacWFFWEM4Z50ZAoRZMXiEggbUqoJVImvpWoHcKUrAzXsBRUlSWUaVvZf3h4RAnKm970Jj7ykY/wD//wDwwGgwVnZDQakSSe8HP99dfznve8h8suu4zLLruM97znPaRpyi//8i8vtv3VX/1Vfuu3fovV1VVWVlb47d/+bZ797GcvpnoeLsrColzDbOzFlOpSEkWGojHkM8G8cgjt0C6gyP2NQVpFrLwBVhj2mWclxjaAN96LggQlhBf+qR2uqKl1g6kseV3hIo2Uhrp2mNoTRE1tMY1ByZDZvKQyDShJICQ68OZvOooQ2vcQTeNHMcHzXBwSZEBjMqraUltHUdcUZeknhoJwYdAFDmybnKgAJyTCuoW7sXDnnC53dVO8opkB4di7vsqLrnw6X7j9NJXwpoNSCgLhiAOBtAZpDFa2js/OIZVAOomQ7apL4hMg50vSZWXYtA6UYmvcMFhtIFUspwqt+xzfPMmw18c1ht7aMkbB9snTXDxa5cj+mr3zkI2541h8gJ/6xVcQuYBD+17BC/ZdStRfIXAV1XjO9Mw9RKGiKAqWV/egwpRJtU0wTAm27qWHQqeC8bhie25Y6jlA0guhF0iWlwacOnGGO0/uUFmFlI5Ap0QyRDq3ENmj/b+SYKx3y46ikCBoJ8WqynNKHAvCIcL7hwrhxyiVOlddeVDnUoBGLD5LA0gI/FlI1E9J1y8izk8T6Aq2Zwx2znBRPMSVc35g8ySnheOubEp18dOZpCto12CNd9D2I7sNSvnKSFGXnrPROKpqjnW1HzeMI0ajEUXhxxDrerfN4NrRVn9DaqqcXq/nCcFFSZzEntA6nRKEAWHgXW9b6Q4vyoVDiIpE9nFaEusUW3nTPNWStm1jaGRNkibIQDEvM6b5jLyeQtgQCu0XBm3y2DQGl3gJcxkpzzto/1+2JnpBGBDFCUhBURU0VYNCEpQWUftqYBwmJElK3EuYZBNyM0ckFqcUotEESrM0XKXI5mzvjAmSCKMskyoj1yW2MoRSE8iAvC7Ymmx70nRaIrQkHGqa0mFbheooToiShKYuvXS8sVSmonIhy0d+gDAKfStOSubZDGcdURS3lQjPNZmM/eTibvsiSVOU9q7rpq4w1ieXA+2TgV6/h1KKsijJ5lPPWRM+cbZtJVAHgX/vyQSp/DSIdY5AOkztCdFhFGCMJ9gKoUh7A5T0yXpTFZRZRuMMUmpC3QpTFqX/OwwYjkYo4SX055mX95/uTKhXC9YP7eOOs/dSzmYIfAVZSoXTijBKcDiUDn3bJo5J+wNsY5hOtyiKmsFghHOWME0Xsv5JmuJcQ5bPsdYnLXEYUdSGpD8gTTRheICm9kmaqWuM0iwtH8CZFzLLM6blvexMKsKwYri8zGRSoJQgHilCLbjr2CZ13dBLHKc3ZmSFYdhbZ33laQTKUfUa6ibj9PYmtfMGfqFqUHFNOa9IpUIq2M5m1CXMq4xkZZXGWXpJgohhMp8ShgFNXXrV2CikrAoqa+kP+ghXstQPQdRYCXEokDIiy0r2DAdI4YiCPv1ej6LMmUzHvhUmLLNJydb0BKM4YDBMuORIzPpqQlNuYTKJZh+ugoaKzZ0CQUOqQ1b2pmyNz6Ijb77rrCFvauIkQciAqj1P4shPAeZjP2If9CKUDcDVzCZzrB097Hv8I0pQPvCBDwDwkpe85LzHP/ShD/GGN7wBgLe+9a3kec6v//qvL4TaPvWpTy00UAD++I//GK01r3zlKxdCbX/913/9iDRQAKyQxLKHKZVfLTQF2/M5yJCl5T6BhMNpSpZbTpwssQ3YyjCfFwgnEQRoqalMhdaCsBcD4AzU85oyr6mERShHhCIUCmMctnaAosxqRGjRyrdimqoC5/9urMMEDqUEETFR4EuixtQ46byOiTFIZ5E4hA48saio6Ccxhoa6veg46xZtAdcSIYXwlRuUxknAijZRsW2rx4IEa72YlhNg8Reoiw+uEEtDgcQJSxgo0kAjWoMwJ7zkvkC0pDrZtjt8EqelAs6x+KWAajKjPxSEQjGZFsggZKtucC5jdSToRw5ZSwprmIqElcEa/VC2/J2Si/YM+OmffxNPu/K5nLr3btKl/aRLq17KHwkKVvY/jcn4WxjnCJKYze0xg2GPre05URAyOrDEqbMly8oSnJHIbEwcKoZxTKgrstLw7XtmlIVCBJ6oPIh9Yi1322f3S1AQdqGoGgTepC3P83YF6idmdnveSiu0Ugti7a6RIDwgQREgeGDG4rGrLSODkN7By2DnOKGeEhuFUT5hOrh/nWgkuGy2zcE77uSbk5L5j7yUe2yNEaCtYz6ft15JfmKsrEvCQGGtTziLsiAIFEWW00/7OGNpjKGqDVEY05iG2SzzTsNhSF5l5POcKIpI04QsyylLn9QofLIRBp5L5ImLfgpNSUkQeufj4cDRT1NMXRIGoVeONYayqnDAdDbjnnvuYWdnG73kEMJQ1jWBDMiyjChKcE4w2xn7xVCkscZR1hVaBGgtqIsGZ5yfvleKQHgLBVs1UFsivCN5pCL2re9neWWFYzunGM93GM+3iaMlNDEhCVE8QOqApD8ijnvcNzlJ40DGEU1TUhUVpvEk4PlsjrEW3Qsp8oI4TFoek0MIRaBcO7br9TZcJehHCtXrEcYxUjiq2pvtKRWiJNRN3ZpN+oTMu/l6roltGu8zhkNrfx2j8Sqt1hqMrdnZ3vLtpcr4CUPlF1OuJcha56grP4GWzXOGoxFJHOOcI89zr1rsHILSOwgbS9LrkcrYV8KUoCwdQRKQBCFKxlSVZdgbUuYZSjjq1gC1ESCFn44zpmH77JS75X9RW5BKs7y8hLWOFSW9xgveBT4MNHVVY50XSMwrr0EShj2CyCfiOOfVaaVXCBeBH+sNwwSpFHVVE8UJ9faW1/hoJKbx4mhNY0kHKY0xzKcTgqDHcvw05jZnZ25wOmbv2iH2L2tiuUry9B5xMOTkkROUzZ1s7GyidMKJkycQasKsqDl7aou1pSFCLnHR2sUo6S8ApZkSR45+pKlLQ14VhCokrxsIQvrSUVWGGYYk8JyBfj+kv7wfZStOnD1BMZ+TGxiuhgQajLAEOiLQigBFXRaoQFCLil4UMOwvsbO9jRKai/YdYHM8ZjafsxQPaGgItGVro0DKM1CvEgUlzkZUegdhEzY3drjzzgmHL16F0RRxSpG7kOlsi/5IIAmJkgBMiCSmri2z6ZQsawgjqGYlx4/NuOKKFcJQY5qc/tA7jT9cPOIWz/8EIQTvete7eNe73vUdt4njmPe///28//3vfyQf/yBoITm+OcPlIVFUE/Qlw2GP2lakyZykpzl72lBXhuVBxPZWRu3wJCankML/AAMhELYVBsJC7XCFIyIEaf0UQ6CoshKMpq79BVQBOgioy4YKS5AGRFJTVQ115ijKiijWuKbG4o2d/FSIQeBXqs4ahKNVVwzIyin9JPKkRevOkV8dvudr8WJsLeltIdRm2hFkLb0MvnQ4JxAY/xoVIVyBELB3bYgWAK3bbkuKm1WOxjoaoRHCcywE/sd8HolZtIRdZ1pWv2R9v+PYVsmZbJPxNGRtPcTYmjQ0SGNJZc3yUohxIbee2KEsLXo04PSOZGdmSFzBZdUYJVPWDh1GhUNvT+BAIogGih3dR4QhgzQCJ+jHJVVVEFDTX93DfD4hCELSNeldi1cSdnLB3cccywPBdm3YKbzmgHA+JlHkRwhVa8houR+R0PlVZJL4kuUuKRRoSbAa5xx13SCkJ8nKVtdcSdUmV48M3vU1Ilp/Gtnx/yIKIwbEkM2Q0hKN9iHiFBel9NcznrG0l71Pfz6fO3EX/7V5GiEkS8lBGlMv2lFl6c3MyrIi0JoyLNBKeE8RFUEKdVWjel4LI89zVkb+2ISTRJGXEQhViDCSvm7oa+P5FNKv/JVsLSYWp4j3KJI2QItlVDSkForB2jpxf4n5fIwcDun1e9Qy4fixe5lMM/atHEQHEqUVucmRQtJPBEmcAFDXjU++stZJuvCVDxuGRHg5/chGmLkhkIlfgNgSGQrMoKFyBXFvRDq6iHEOp89MSII1tBhCJQhVjNIhtRFoN6A/2k8lBJtbJSFD8mnOcm8JQ4WwgiSOWTpwAOscRksa1fipGGURmUYIKPMSg9dzKauCIssRQhCEIVVTk6Zp64CtiFrXYy9gp8myGUhFXeTUtURrT7Rt6orGNEjf021bcwWhUljjqKqSum78CPBoRBzHaO1tO+q68MJmSYzS3tFct5WVMAjRQYCpK5y1NGVO0/hKrwSqpoK6QOuAMs+8NouIGKWSnbLC1SWBEsRxwnQ2pW5qzwNxXmoyCRUVK1QzRRBqRn1fwcVZbEvOLooC8L8rKSVlXiCVJo7jtvrjL4hB2x7cbbE6KZjsbCFFez4HGlM3NLVvuUn8uarDkHxnB6cCZrMJSZKQz+d+f5J1ZKh57tFDpFFKNs3BCgb9If3lNXbOnOXitSWi5LnkzQ533PtF+n3BaGkvNHPs09cZDS9isHY5t972aYStKQtLFAf0l3sMBpKTJ7fRacBK0GM6n/rkioo48AvbosgJghAtBeV8i40zmywt9UkGknmWEwV++k0HEUnUoyhKqukmoyRgq2ioc8OscVRmh51ZhhISKQKG/WWSaECvH1HLBmt3CKKG0ubce+oUl166h/171ijLHaydcPCiFdKlgM1TO2RZj0qWDPoR/bjHZGtKXVUMhholKpJhCLpk2PMTWYSK0XAPRo6ZTjcIVcxWPsZxP2X0h4GntBfP2Y2ccm69AmRPcvmle6gLyfZ4TDbDk0ULy8XDIf1UMF9OuG+z4NRWzXRakkQKKSzCSExTUTUVwzAhUgk7ZuYNx2TbtxWOWjrvs9ASJpXyJFqBF/UpK4PD+L6iUpg6oJiXrZW5Vzu0eKtsTUPjGmxdY2m86BW7rsWe/Z5nJQwHi2kc4cSuAr2XjRbedEtYA87ilAOp2/Fi6xMUJ3AG7/1jC2iVBpvGtj926dtKkVdgbKxAOD+K6nzfiDaXoWXJUjeW3emW3RVe2l9ljZz+3iWQezl75m4GgYXG0Asj/ABKjbQZ+5dj7tvxBlnHT1XMneb5V14F0n9mmA5Rrt0PFM4FCDlgdf3ZbJ78Cja/h3k2ZrqzhakalkdrREHIqWlGJAYorZiKAGTEak+zNHSsjgTfvneOVQaFRKDROvKqqNJLke9yQFx7eFVVIKWkKArqhQeJptfrtRMfDXWreSNFq1Sstff1CAMvqvcIsKvMipDEy3vJBysksaLOLaosMFWOTSNSaZDTgDSI6R2+jAPJCj91yRKz2U1sBgkvvfKaRQXHVzP8d3j/C4Nsvz/fEjp/8bFLkfFT625XB3AhSb+70fn5l6/w7Vb6hDi3rRS+siQcGEAlEcPYE+SLWnH8VA5yhRc+86Ug/BSV/0gvVGjb5Hj3GJxXRrzffvtEyQBucTyLowGHlxwwIG2rK6Q1tnEc3vccDkqDExbpNNJJlFUopxFWcuJMjpOOQO7lGRevYoQ36BRtNUrefzloz32wdY7jd53yLR1rsLZhOq0ZDPpESwnz+ZSqLFHasjmfIoSkqqpWstxfB/LcEmrZCqMFpL2er9o1Ndls3FZcNI31FSxnLXXbqozjhCiKidOEffv2sXH6JNYK8iKnbFt6Qip6vQTnIoSAbDbGJGn7uh7g2C4qhDAMRku7XGxq40d7nYMoHmCamq3NHaI4QYXe/HCeZYRRyHDYZ2d7gpCC2XSHjZ1tVtcPsrx3nbquqZsKLUMQgqasAEl/MPB8wHbB0BtorPXXLde2CL1r8rz9jWo/sRMEXvemKABaErAgUH4acrq9jXNDwkASJTGbZ87wuc98hl6vzzOe9QxGoyGmdkRuhemZnO1qzJnTp3HOc8/6/T4HDx+GesbZnYKLLrmMF135f3Hnff8fm7PbcYFhef35rC1fzPb4Ti5/xoCi8LYh0+wMTVOzNArp64TJWDErLSUBTS1wQtFUc+JeTJkZdABhYH37UDl6wz62rnHWeM6arpmXlgCIVMCsUJyYGIYrQ8Y7W+w9uAfrStb27EFkOYOgpnAF6bCHDi2u9h50SRxRVBWxdqiyYGfnpK9IGUGtGqRuiAcJzkniXkYcNSSRJO4PqWuLaSK0VmjtNbcIHZGOKDOHUoY9+0K0TlFNhs0bTCWf+CmeCwUnNwuSRNPfo1nbE2BsxdkzGaNhSF4bXCUQ1GxU22TKS2U7BWlUc2Q5ZW15mZOTnM1ZRZ6XDE0flzVkLsMFghJLbiqUUEjnlTN1oLF1hUAQxgmh0lTNDGsa6sK3borSk82skzgpEGHA3v2HEFL56oaQKEA5Q9nMfQk0VFhjUYGirhus8mRGsXuTa+W5XTu2J9CegyL9SBdO4JRckGb9NE8DTgM11hWtxociTXstcbdBt5yJBol1zWIVc84QcPfj22kgHM76v23LiWms42TuWHvGi1jfexn55jbbW8dZWzbkk5JBGHniXKPp9QTLOmAy3eSypYBxbZj1V3nW5S/k8ude7bVorFjIugDYVsNGhjH9pX0U5QlkoFjevw+DIFI9phv3EMXLFA1UZcHQlmRZRJJCGJYMkjXOjo8jnMPJ3RFatein7x6pwI/U1nXDbDYnL/LF5FUYhsRxTBzHGGMWCUsQ+JtHGEaglOeTxCFCPYJa5v0gcARxSNgbIDNLFG2joxg3PgNpgiPFKVBpQm/fYVQIIxdx6epBpvp2Lj/63Ae0S+WiArbbuvMtTrn4bhfeSu255hOUtq0ozHnnhEDgHRR3Tw5289VzcVxkLw52q4VSYoW/gUp7Lt67mY6w59pf55IM/+YPPB/9a8XiTfzQ1bnv8YEZmRMWX0pstYT8zG1LjBZtHrP7+nP/tcKC2I3Z7m9x97N3D7z9yPtdeBvTcPbkmMGgz9hU9Hp9lNLs2bePO795B3EckayukM0zkuUlz+eJQtb27PGaNXjBxzCMyLM5wyhk7/o69951J5tnNpjNZzgEvbRHnCQ01IRBgHVeC2aXv9LUDZtnz6J12E61eM2Sosho6pJKQ11b0iSmcJK6toTRLo/Jcz+GyyOk0sxnc+raEKd9n6g7ixS+7WnjnufwSMlsNiNOU7CWfF6gJIxWVonCgMmsAOk4e+ZUK0DpqPIpxjqcdZRlAQjm87mvWgxSBI6msTitqWs/YRYEmqZp6PWGxEmCdZa1tb0orTxvqmkwTcPW2TO+wqcbnBAUWYFIAuJIM0hT1tdW+OJN/y87421e8pKXkPaH5NmEydaUKArYf+AAG6c3yOcZpqopipKjlx5hz/rQmx7KmEv3/zBL0z7HNu6kmiu+vf1FdHwWKyTG1t4sVvfI85Kd8QaDNGJSNczmhrqwRHHCeJazM5myJvokaZ+obyiqMav7hixXET01wGiDLS2Tac5gtEYYjNna3uHo/gNIm3Ds9EmGo/1EwyFCa+Y7myAizmxnFL2K9cMpSc/7AEVJymC4Ql3XFNsZUlQM+ytYVZOPDYgE5yaEaUgvVcxmhskk4/h9O6ys9hBAEhlM4xXPHX7EPtAxKhjSDyXT2QRsjB70KOucYW8F2Vdsn/ou6KBcCJBBTElNFDiMhNMnc06eqTAohPNjq0JG2NywM/arh36s2b/cY3V5xMmzO2yPDXljSHoxK0HKptjxviQyoKxLYutXVdJ6AqqrLWnoWwORjv2q0FREUYJoKsrSYowXCgqDgNo6BsNlDh6+2CcoTYNwDVo0OJMjXYlQCUJJtAwIAi93783GA6oGtHFI1V4MReCrJGiECPxq0jqcU4AG61eqWOf78c6BtdjaIGWIMYIkThmkPbbnjVeyFVA0FtFKJsO5i/8u54WWUOuJpO1NROJHDkVA3Xseey7/vwn6KyjzZRKpUTonTh1NndGLQhLlGM+Nr/rIgNVew0Bp5ocv4+k/9IOoMIbaYJ1tkx//OY20SAdGGLbnO5Tb2zRANBhQacmktoTDlHqmGW+PGSSOg2sR29Nt1lYOstRXFColy7xqqZ+mkjzjmc9gdXWZra0tirL0AnxVjXGGhpJeP6XXT7HWLgwSVcuxkFLS66Vo7Ymzg/6AMAxABd63SUpM49UcdxOBhweHcX6cnXiAqWucVgTSkp++l8HhS2mswQA26BP0htTWIqzlwGiZO+KIPYf2oHVwv/d88IpFOPmQjz/U/rj2Jn3/u79wD80Xe+DiSDqBNr46Zdt/vv5RPeA9acu/YrHH59/+H7xfD4RqbSPOR2uiKbyooU+2XLv/bavPyfbzPJHUC0n5ylFLnUY4cX6S8qC9EVhxjsPU1I1vPTgQKkAHEQg4feo0QgbeRyjtUZU1CEGaplgcO9tbxHHMPMtah2cv7NbrDZjP5kRRQm8wpD9cahcqjsYYgjAmTWPmsyk6DCnzHNs0JNKTyq2x5JlfSCEkYRQTBFDXpiUVB6zsWfflQ2dYWtmDkorNzQ1fXS4Lf34TkrQjvc5astkU5wQIQV2WNLXANBV1qXFaIbBUZcl4ZxMBHDl6MU1VIVuuXlkWVFWDsZZAa5LEJ/r9wRI4zwHL5nOU8uPDs7xieXmZUHreWN1UuLlBhQFnNzfbBUS0WFREceKn6aZzJlunOXbntzl9+gzPes6zOHzxES4+egmXP+e5fO2rt/P1b3yDfevrrK6teqVcoSnzOYFWqF7KaGkJBMyzgv6wTz4fg4PpdIoK95LoMdvTr2PclDPTMWWtkHVEUxniXsM0q8hzw6njm4S9ASJyTKc7TGczGgvL+3osL/fZ2pwx3Zpi6pJhb0CZF9Syh9QJSsYMUoMUAWvLKU29iQsrhntTDtkhpd1ChiGmqrANTKYZo8EqMig5sTHmyEWOpWU/Qo0IGfQELhboZIQMA0QjWVoNEXpIlkFT+jgQ5Lg6ot/vMZ7ssGd1wM6sIRCSqsyJ+haUxpYpm2dPU1YZ/dEQa0r26WW2NxO2J6dZ3bPaClw+PAj3cIglFxjG4zFLS0v8P2/8VcIoaMvZ/oZsHejWu8TuTre0lzpjfMYfBxonoHEW2wh/EVACLSWNddhWA+Pcxcl/7qK6jV8h7nJH/MimXOiGOFjwDzxbPqTXH/qWzKLsvStn7y/Xu+PD3pfBt/Nl64Uh1G7rR7T8j/szOd25Hbt/zX3RC3rw12uqmlMbY+rGE5bkA152/7Xvd8K5xa1gMBwRakWc9j2fpcopszF+MNsbwinZKq4aL6tiLMRhu9IOE9L+CP6HG7lzhjqfgKkW+jCN9Wx8KSxNdY6c6SdRPJmwMhakZpaVrQaZj+VoOCRNU+q2VVPXdVsZMecqDu7+7s271SX5oMkcP26sFsGTUrI0Gt6vkvBI4bBVjjDGc5i8YYqvpDiHa3VAZNpHSL/OMM4xm04ZDPqP4XMfZ7TVhgefS9/h7Hosu+0e/HL3UH8tvsr/4cMezg/hobbH/+53J29w3tRz97eoWodygdfqsM7zouwDfquem+G9cbxysV+22LYH6axdfJZUEq2UHyG3rr32iUWrz1dG28TLsZj22n2tbO08ds/3MPSk57rloljnUO0k4SJ8joV/z7kKq29veSn9c1UvX73j3Ih++3ks3scu2o2LxZBzCwfx3fDuvgftwqldO7UVNE+UXXwdwnPsRMuxi0M/Sl9VJarltPhLqn/NPMsASJPEe7c1u2T4c3EUwgdA7n6fbZvRWbA0WLxBaNPqJPlKowDhME0DwpPzfdve3yuaxp8DuuUDOef8aLS17fdu/HXV+nPHGu+qHIR+wgq8a3ldNjS2buPnd8/f89oEXFrvr6R3dbV8ko5r2uNrxdnaCqNzBpzwjsvWLPbXWEPQGlbunsMOhw40SgYY22BM+33i0CrylS9hkFKRzQ1//Md/ws7ODqPR6P/8k3oqJijHjx/noosuerJ3o0OHDh06dOjwKHDs2LHzhF4fCk/JBMVayx133MEzn/lMjh07xnA4fLJ36XsKu15HXWyfGHTxfeLQxfaJRRffJxbfD/F1zjGdTjlw4MD5JPOHwFOSgyKl5ODBgwAMh8Pv2S/yyUYX2ycWXXyfOHSxfWLRxfeJxfd6fP+n1s4uHt2YQYcOHTp06NChwxOILkHp0KFDhw4dOlxweMomKFEU8c53vpMoip7sXfmeQxfbJxZdfJ84dLF9YtHF94lFF9/z8ZQkyXbo0KFDhw4dvrfxlK2gdOjQoUOHDh2+d9ElKB06dOjQoUOHCw5dgtKhQ4cOHTp0uODQJSgdOnTo0KFDhwsOXYLSoUOHDh06dLjg8JRMUP78z/+co0ePEscxV111Ff/+7//+ZO/SBY/3vve9vPCFL2QwGLB3715+7ud+jjvuuOO8bZxzvOtd7+LAgQMkScJLXvISvva1r523TVmWXHvttaytrdHr9fjZn/1Zjh8//t08lAse733vexFCcP311y8e62L72HDffffxmte8htXVVdI05XnPex633HLL4vkuvo8eTdPwe7/3exw9epQkSbjkkkv4/d//fez9bGe7+D58fO5zn+NnfuZnOHDgAEII/v7v//685x+vWG5vb/Pa176W0WjEaDTita99LTs7O0/w0X2X4Z5i+OhHP+qCIHAf/OAH3de//nV33XXXuV6v5+65554ne9cuaPzUT/2U+9CHPuS++tWvuttuu81dc8017vDhw242my22ed/73ucGg4H72Mc+5m6//Xb3i7/4i27//v1uMpkstnnjG9/oDh486G644Qb35S9/2f34j/+4e+5zn+uapnkyDuuCw8033+wuvvhi95znPMddd911i8e72D56bG1tuSNHjrg3vOEN7ktf+pK766673Kc//Wn33//934ttuvg+evzBH/yBW11ddf/0T//k7rrrLvd3f/d3rt/vuz/5kz9ZbNPF9+Hjn//5n9073vEO97GPfcwB7hOf+MR5zz9esXz5y1/urrjiCveFL3zBfeELX3BXXHGFe8UrXvHdOszvCp5yCcoP/uAPuje+8Y3nPXb55Ze7t7/97U/SHj01sbGx4QB34403Ouecs9a69fV19773vW+xTVEUbjQaub/4i79wzjm3s7PjgiBwH/3oRxfb3HfffU5K6f7lX/7lu3sAFyCm06m77LLL3A033OB+7Md+bJGgdLF9bHjb297mrr766u/4fBffx4ZrrrnG/cqv/Mp5j/38z/+8e81rXuOc6+L7WPDABOXxiuXXv/51B7gvfvGLi21uuukmB7hvfOMbT/BRfffwlGrxVFXFLbfcwste9rLzHn/Zy17GF77whSdpr56aGI/HAKysrABw1113cerUqfNiG0URP/ZjP7aI7S233EJd1+dtc+DAAa644oou/sCb3vQmrrnmGn7yJ3/yvMe72D42fPKTn+QFL3gBv/ALv8DevXu58sor+eAHP7h4vovvY8PVV1/Nv/7rv/LNb34TgK985St8/vOf56d/+qeBLr6PJx6vWN50002MRiN+6Id+aLHNi170Ikaj0fdUvJ9SbsZnz57FGMO+ffvOe3zfvn2cOnXqSdqrpx6cc7zlLW/h6quv5oorrgBYxO+hYnvPPfcstgnDkOXl5Qdt8/0e/49+9KN8+ctf5j/+4z8e9FwX28eGO++8kw984AO85S1v4Xd/93e5+eab+Y3f+A2iKOJ1r3tdF9/HiLe97W2Mx2Muv/xylFIYY3j3u9/Nq171KqA7fx9PPF6xPHXqFHv37n3Q++/du/d7Kt5PqQRlF0KI8/52zj3osQ7fGW9+85v5z//8Tz7/+c8/6LlHE9vv9/gfO3aM6667jk996lPEcfwdt+ti++hgreUFL3gB73nPewC48sor+drXvsYHPvABXve61y226+L76PC3f/u3fPjDH+YjH/kIz3rWs7jtttu4/vrrOXDgAK9//esX23XxffzweMTyobb/Xov3U6rFs7a2hlLqQRnixsbGgzLSDg+Na6+9lk9+8pN85jOf4dChQ4vH19fXAf6PsV1fX6eqKra3t7/jNt+PuOWWW9jY2OCqq65Ca43WmhtvvJE//dM/RWu9iE0X20eH/fv388xnPvO8x57xjGdw7733At25+1jxO7/zO7z97W/nl37pl3j2s5/Na1/7Wn7zN3+T9773vUAX38cTj1cs19fXOX369IPe/8yZM99T8X5KJShhGHLVVVdxww03nPf4DTfcwItf/OInaa+eGnDO8eY3v5mPf/zj/Nu//RtHjx497/mjR4+yvr5+XmyrquLGG29cxPaqq64iCILztjl58iRf/epXv6/j/9KXvpTbb7+d2267bfHvBS94Aa9+9au57bbbuOSSS7rYPgb8yI/8yING4r/5zW9y5MgRoDt3HyuyLEPK828FSqnFmHEX38cPj1csf/iHf5jxeMzNN9+82OZLX/oS4/H4eyveTwYz97Fgd8z4r/7qr9zXv/51d/3117ter+fuvvvuJ3vXLmj82q/9mhuNRu6zn/2sO3ny5OJflmWLbd73vve50WjkPv7xj7vbb7/dvepVr3rI8bdDhw65T3/60+7LX/6y+4mf+Invy1HC/wn3n+JxrovtY8HNN9/stNbu3e9+t/vWt77l/uZv/salaeo+/OEPL7bp4vvo8frXv94dPHhwMWb88Y9/3K2trbm3vvWti226+D58TKdTd+utt7pbb73VAe5//a//5W699daFFMbjFcuXv/zl7jnPeY676aab3E033eSe/exnd2PGFwL+7M/+zB05csSFYeie//znL0ZlO3xnAA/570Mf+tBiG2ute+c73+nW19ddFEXuR3/0R93tt99+3vvkee7e/OY3u5WVFZckiXvFK17h7r333u/y0Vz4eGCC0sX2seEf//Ef3RVXXOGiKHKXX365+8u//Mvznu/i++gxmUzcdddd5w4fPuziOHaXXHKJe8c73uHKslxs08X34eMzn/nMQ15rX//61zvnHr9Ybm5uule/+tVuMBi4wWDgXv3qV7vt7e3v0lF+dyCcc+7Jqd106NChQ4cOHTo8NJ5SHJQOHTp06NChw/cHugSlQ4cOHTp06HDBoUtQOnTo0KFDhw4XHLoEpUOHDh06dOhwwaFLUDp06NChQ4cOFxy6BKVDhw4dOnTocMGhS1A6dOjQoUOHDhccugSlQ4cOHTp06HDBoUtQOnTo0KFDhw4XHLoEpUOHDh06dOhwwaFLUDp06NChQ4cOFxz+f+2h+Ay9LOqmAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names = ['羊驼','熊猫']\n",
    "imgs,labels = next(iter(train_loader))\n",
    "for i in labels:\n",
    "    print(class_names[i])\n",
    "\n",
    "imshow(torchvision.utils.make_grid(imgs))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T02:40:22.282359100Z",
     "start_time": "2023-11-20T02:40:15.469224600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 准备alexnet模型"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlexNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): ReLU(inplace=True)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Dropout(p=0.5, inplace=False)\n",
      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "alexnet = torchvision.models.alexnet(pretrained=True)\n",
    "print(alexnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T04:52:18.279079Z",
     "start_time": "2023-11-20T04:52:17.072992900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [],
   "source": [
    "for p in alexnet.parameters():\n",
    "    p.requires_grad = False\n",
    "\n",
    "alexnet.classifier = nn.Sequential(\n",
    "    nn.Dropout(0.5,inplace=False),\n",
    "    nn.Linear(9216,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Linear(4096,2,bias=True)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T04:52:19.607314200Z",
     "start_time": "2023-11-20T04:52:19.213316300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [],
   "source": [
    "import datetime\n",
    "def train_loop(model,loss_fn,optimizer,n_epochs,train_loader):\n",
    "    for epoch in range(1,n_epochs+1):\n",
    "        loss_train = 0.0\n",
    "        for i,data in enumerate(train_loader,1):\n",
    "            imgs,labels=data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            loss = loss_fn(outputs,labels)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            loss_train+=loss.item()\n",
    "            if i % 10 ==0:\n",
    "               print(\"{}, Epoch:{}, i:{}, 训练损失:{}\".format(datetime.datetime.now(),epoch,i,loss_train/50))\n",
    "               loss_train=0.0"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T04:53:40.751002100Z",
     "start_time": "2023-11-20T04:53:40.721011500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-20 12:53:48.188659, Epoch:1, i:10, 训练损失:0.0010793664946686476\n",
      "2023-11-20 12:53:48.579661, Epoch:1, i:20, 训练损失:0.0007546706884750165\n",
      "2023-11-20 12:53:49.298663, Epoch:1, i:30, 训练损失:0.0008266496205033036\n",
      "2023-11-20 12:53:49.682663, Epoch:1, i:40, 训练损失:0.002540866033450584\n",
      "2023-11-20 12:53:50.116667, Epoch:1, i:50, 训练损失:0.0010237539903027936\n",
      "2023-11-20 12:53:50.501660, Epoch:1, i:60, 训练损失:0.014938542896852596\n",
      "2023-11-20 12:53:51.195661, Epoch:1, i:70, 训练损失:0.005301341676040465\n",
      "2023-11-20 12:53:51.561663, Epoch:1, i:80, 训练损失:0.007025759428506717\n",
      "2023-11-20 12:53:57.400667, Epoch:2, i:10, 训练损失:0.0002580278604546038\n",
      "2023-11-20 12:53:57.818688, Epoch:2, i:20, 训练损失:0.0010965931283135433\n",
      "2023-11-20 12:53:58.191660, Epoch:2, i:30, 训练损失:0.0005432322242631926\n",
      "2023-11-20 12:53:58.573672, Epoch:2, i:40, 训练损失:0.016205768882427946\n",
      "2023-11-20 12:53:58.951662, Epoch:2, i:50, 训练损失:0.002703138428478269\n",
      "2023-11-20 12:53:59.333665, Epoch:2, i:60, 训练损失:0.0023224845791992264\n",
      "2023-11-20 12:53:59.707665, Epoch:2, i:70, 训练损失:0.006608477112895344\n",
      "2023-11-20 12:54:00.069709, Epoch:2, i:80, 训练损失:0.0025541521712148098\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(alexnet.parameters(),lr=0.001,momentum=0.9)\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "train_loop(model=alexnet.cuda(),loss_fn=loss_fn,optimizer=optimizer,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T04:54:00.704665Z",
     "start_time": "2023-11-20T04:53:42.781187300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "def test_loop(model,test_loader):\n",
    "    correct =0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in test_loader:\n",
    "            imgs,labels = data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            _,preds = torch.max(outputs,dim=1)\n",
    "            correct+=int((preds==labels).sum())\n",
    "            total += labels.shape[0]\n",
    "    print('精度:{:.3f}%'.format(correct/total*100))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T03:06:54.744137300Z",
     "start_time": "2023-11-20T03:06:54.693124900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:97.087%\n"
     ]
    }
   ],
   "source": [
    "test_loop(alexnet.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T04:52:55.951241900Z",
     "start_time": "2023-11-20T04:52:48.795239100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(torchvision.utils.make_grid(imgs))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T03:08:25.922084800Z",
     "start_time": "2023-11-20T03:08:24.139323900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "羊驼\n",
      "羊驼\n",
      "羊驼\n",
      "羊驼\n",
      "熊猫\n"
     ]
    }
   ],
   "source": [
    "outputs = alexnet.cuda().eval()(imgs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "for i in preds:\n",
    "    print(class_names[i])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T03:09:53.353325800Z",
     "start_time": "2023-11-20T03:09:53.229323400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 使用vgg"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "vgg = torchvision.models.vgg16(pretrained=True)\n",
    "print(vgg)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T04:54:07.963292300Z",
     "start_time": "2023-11-20T04:54:06.480299900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [],
   "source": [
    "for p in vgg.parameters():\n",
    "    p.requires_grad = False\n",
    "\n",
    "vgg.classifier = nn.Sequential(\n",
    "    nn.Linear(25088,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,2,bias=True)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T04:54:09.949828Z",
     "start_time": "2023-11-20T04:54:09.067818400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-20 12:54:17.919484, Epoch:1, i:10, 训练损失:0.11864712178707122\n",
      "2023-11-20 12:54:19.569530, Epoch:1, i:20, 训练损失:0.06342806130647659\n",
      "2023-11-20 12:54:21.227482, Epoch:1, i:30, 训练损失:0.038586919084191325\n",
      "2023-11-20 12:54:22.890550, Epoch:1, i:40, 训练损失:0.021023826338350773\n",
      "2023-11-20 12:54:24.565529, Epoch:1, i:50, 训练损失:0.012483875572215766\n",
      "2023-11-20 12:54:26.240481, Epoch:1, i:60, 训练损失:0.011748258285224437\n",
      "2023-11-20 12:54:27.917508, Epoch:1, i:70, 训练损失:0.006998052531853319\n",
      "2023-11-20 12:54:29.584529, Epoch:1, i:80, 训练损失:0.0018136447499273345\n",
      "2023-11-20 12:54:36.213528, Epoch:2, i:10, 训练损失:0.0010549632075708359\n",
      "2023-11-20 12:54:37.860527, Epoch:2, i:20, 训练损失:0.0005583855375880376\n",
      "2023-11-20 12:54:39.512479, Epoch:2, i:30, 训练损失:0.003415284437360242\n",
      "2023-11-20 12:54:41.168532, Epoch:2, i:40, 训练损失:0.0017003260063211201\n",
      "2023-11-20 12:54:42.829482, Epoch:2, i:50, 训练损失:0.0032257535342796475\n",
      "2023-11-20 12:54:44.507482, Epoch:2, i:60, 训练损失:0.0010843327706788841\n",
      "2023-11-20 12:54:46.182508, Epoch:2, i:70, 训练损失:0.0020136325032581227\n",
      "2023-11-20 12:54:47.847529, Epoch:2, i:80, 训练损失:0.0008947388557135127\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(vgg.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=vgg.cuda(),loss_fn=loss_fn,optimizer=optimizer,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T04:54:48.513482600Z",
     "start_time": "2023-11-20T04:54:11.097303100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:99.515%\n"
     ]
    }
   ],
   "source": [
    "test_loop(vgg.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T04:55:03.539933500Z",
     "start_time": "2023-11-20T04:54:52.695958200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 残差网络"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet101_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet101_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (6): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (9): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (13): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (14): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (15): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (16): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (17): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (18): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (19): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (20): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (21): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (22): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "resnet = torchvision.models.resnet101(pretrained=True)\n",
    "print(resnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T07:03:20.750862700Z",
     "start_time": "2023-11-20T07:03:16.334530400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [],
   "source": [
    "for p in resnet.parameters():\n",
    "    p.requires_grad = False\n",
    "resnet.fc = nn.Linear(2048,2,bias=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T07:39:40.484703500Z",
     "start_time": "2023-11-20T07:39:40.453706400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-20 15:41:16.896678, Epoch:1, i:10, 训练损失:0.13095127046108246\n",
      "2023-11-20 15:41:18.079676, Epoch:1, i:20, 训练损失:0.09640340805053711\n",
      "2023-11-20 15:41:19.188701, Epoch:1, i:30, 训练损失:0.06399043545126915\n",
      "2023-11-20 15:41:20.295680, Epoch:1, i:40, 训练损失:0.05633269399404526\n",
      "2023-11-20 15:41:21.423678, Epoch:1, i:50, 训练损失:0.053041615411639216\n",
      "2023-11-20 15:41:22.534677, Epoch:1, i:60, 训练损失:0.04130826499313116\n",
      "2023-11-20 15:41:23.672678, Epoch:1, i:70, 训练损失:0.028728618919849395\n",
      "2023-11-20 15:41:25.626225, Epoch:1, i:80, 训练损失:0.03884225677698851\n",
      "2023-11-20 15:41:35.375368, Epoch:2, i:10, 训练损失:0.07107030477374793\n",
      "2023-11-20 15:41:36.539414, Epoch:2, i:20, 训练损失:0.01050092525780201\n",
      "2023-11-20 15:41:37.644365, Epoch:2, i:30, 训练损失:0.04028492525219917\n",
      "2023-11-20 15:41:38.755369, Epoch:2, i:40, 训练损失:0.06061464216560125\n",
      "2023-11-20 15:41:39.860384, Epoch:2, i:50, 训练损失:0.022678243778645992\n",
      "2023-11-20 15:41:40.988400, Epoch:2, i:60, 训练损失:0.050336814969778064\n",
      "2023-11-20 15:41:42.132724, Epoch:2, i:70, 训练损失:0.037329242937266825\n",
      "2023-11-20 15:41:43.226734, Epoch:2, i:80, 训练损失:0.047269910275936126\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(resnet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(resnet.cuda(),loss_fn=loss_fn,optimizer=optimizer,train_loader=train_loader,n_epochs=2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T07:41:43.922710200Z",
     "start_time": "2023-11-20T07:40:34.666703500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:95.146%\n"
     ]
    }
   ],
   "source": [
    "test_loop(model=resnet.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T07:43:03.439703700Z",
     "start_time": "2023-11-20T07:42:52.688336400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 使用squeezenet网络"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SqueezeNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (3): Fire(\n",
      "      (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Fire(\n",
      "      (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (6): Fire(\n",
      "      (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Fire(\n",
      "      (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (9): Fire(\n",
      "      (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Fire(\n",
      "      (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Fire(\n",
      "      (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Fire(\n",
      "      (squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Conv2d(512, 1000, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=SqueezeNet1_1_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_1_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "squeezenet = torchvision.models.squeezenet1_1(pretrained=True)\n",
    "print(squeezenet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T07:54:21.062937Z",
     "start_time": "2023-11-20T07:54:20.804864700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [],
   "source": [
    "for p in squeezenet.parameters():\n",
    "    p.requires_grad = False\n",
    "squeezenet.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Conv2d(512,2,kernel_size=(1,1),stride=(1,1)),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.AdaptiveAvgPool2d(output_size=(1,1))\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T08:01:19.578486800Z",
     "start_time": "2023-11-20T08:01:19.371566100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-20 16:03:13.723267, Epoch:1, i:10, 训练损失:0.10810738176107407\n",
      "2023-11-20 16:03:14.252269, Epoch:1, i:20, 训练损失:0.04170699629932642\n",
      "2023-11-20 16:03:14.397262, Epoch:1, i:30, 训练损失:0.03121933987364173\n",
      "2023-11-20 16:03:14.767271, Epoch:1, i:40, 训练损失:0.017383671067655086\n",
      "2023-11-20 16:03:15.457263, Epoch:1, i:50, 训练损失:0.020245701274834573\n",
      "2023-11-20 16:03:15.747267, Epoch:1, i:60, 训练损失:0.008095333473174832\n",
      "2023-11-20 16:03:16.146262, Epoch:1, i:70, 训练损失:0.002023178256349638\n",
      "2023-11-20 16:03:16.860493, Epoch:1, i:80, 训练损失:0.003349397019483149\n",
      "2023-11-20 16:03:22.234831, Epoch:2, i:10, 训练损失:0.02189494199818\n",
      "2023-11-20 16:03:22.865833, Epoch:2, i:20, 训练损失:0.009918338428251445\n",
      "2023-11-20 16:03:23.142844, Epoch:2, i:30, 训练损失:0.0010474791747401469\n",
      "2023-11-20 16:03:23.846837, Epoch:2, i:40, 训练损失:0.005444223678496201\n",
      "2023-11-20 16:03:24.074833, Epoch:2, i:50, 训练损失:0.00475755738792941\n",
      "2023-11-20 16:03:24.344834, Epoch:2, i:60, 训练损失:0.0018814039893914015\n",
      "2023-11-20 16:03:24.607834, Epoch:2, i:70, 训练损失:0.0012518633002764545\n",
      "2023-11-20 16:03:25.058844, Epoch:2, i:80, 训练损失:0.014332395045785233\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(squeezenet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=squeezenet.cuda().eval(),loss_fn=loss_fn,optimizer=optimizer,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T08:03:25.650832800Z",
     "start_time": "2023-11-20T08:03:01.019604900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:98.058%\n"
     ]
    }
   ],
   "source": [
    "test_loop(squeezenet.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T08:03:55.211248600Z",
     "start_time": "2023-11-20T08:03:47.853572300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:98.058%\n"
     ]
    }
   ],
   "source": [
    "test_loop(squeezenet.cuda().eval(),test_loader=valid_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-20T08:04:28.962802100Z",
     "start_time": "2023-11-20T08:04:20.334208500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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